# Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders

Souhaila Khalfallah, William Puech, Mehdi Tlija, Kais Bouallegue

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Souhaila Khalfallah, William Puech, Mehdi Tlija, Kais Bouallegue. Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders. IEEE Access, 2025, 13, pp.17002-17015. 10.1109/ACCESS.2025.3532515. lirmm-05288133

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# Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders

SOUHAILA KHALFALLAH<fnref n="1" />,<fnref n="2" />, WILLIAM PEUCH<fnref n="3" />,(SENIOR MEMBER, IEEE), MEHDI TLIJA<fnref n="4" />, AND KAIS BOUALLEGUE<fnref n="5" />,

Corresponding author: First A. Author (e-mail: souhailakhalfallah7@gmail.com/souhaila.khalfallah@eniso.u-sousse.tn).

**ABSTRACT** Neurological disorders are among the leading causes of both physical and cognitive disabilities worldwide, affecting approximately 15% of the global population. This study explores the use of machine learning (ML) and deep learning (DL) techniques in processing Electroencephalography (EEG) signals to detect various neurological disorders, including Epilepsy, Autism Spectrum Disorder (ASD), and Alzheimer's disease. We present a detailed workflow that begins with EEG data acquisition using a headset, followed by data preprocessing with Finite Impulse Response (FIR) filters and Independent Component Analysis (ICA) to eliminate noise and artifacts. Furthermore, the data is segmented, allowing the extraction of key features such as Bandpower and Shannon entropy, which improve classification accuracy. These features are stored in an offline database for easy access during analysis, to be then applied for both ML and DL models, systematically testing their performance and comparing the results to prior studies. Hence, our findings show impressive accuracy, with the random forest model achieving 99.85% accuracy in classifying autism vs. healthy subjects and 100% accuracy in distinguishing healthy individuals from those with dementia using Support Vector Machines (SVM). Moreover, deep learning models, including Convolutional Neural Networks (CNN) and ChronoNet, demonstrated accuracy rates ranging from 92.5% to 100%. In conclusion, this research highlights the effectiveness of ML and DL techniques in EEG signal processing, offering valuable contributions to the field of brain-computer interfaces and advancing the potential for more accurate neurological disease classification and diagnosis.

**INDEX TERMS** Electroencephalography (EEG), Neurological disorders, Machine learning, Deep learning

## I. INTRODUCTION

The study of EEG signals plays a crucial role in understanding the complex functioning of the brain and its impact on cognitive and motor functions, as well as sensory processing. This article aims to provide a comprehensive overview of EEG signals and their characteristics in the context of brain-computer interfaces (BCIs) [1]–[3] for the diagnosis of pathological illnesses [4]–[7]. The structure and functioning of neurons, which form the fundamental units of the brain, are explored, highlighting the transmission of

electrical signals through axons and dendrites. These signals, known as action potentials, form the basis of electrical activity in the brain and are responsible for communication and transfer between neurons.

EEG signals are the result of the activation of pyramidal neurons in the cortical layer of the brain. These signals are measured on the scalp's surface and provide valuable insights into brain activity. However, the electrical fields generated by these signals are relatively weak and require sophisticated techniques to capture and analyze them accurately. EEG

<footnote n="1">¹Department of Electrical Engineering, National School of Engineering of Sousse, Sahloul Ring Road, Sousse, 4054, Tunisia.</footnote>

<footnote n="2">²Laboratory of Electronic and Microelectronic, Faculty of Sciences of Monastir, Environment Avenue, Monastir, 5019, Tunisia.</footnote>

<footnote n="3">³LIRMM, Université Montpellier, CNRS, 860 rue de St Priest, Montpellier, 34095, France.</footnote>

<footnote n="4">⁴Industrial Engineering Department, College of Engineering, King Saud University, Riyadh, 11421, Kingdom of Saudi Arabia.</footnote>

<footnote n="5">⁵Department of Electrical Engineering, Higher Institute of Applied Sciences and Technology of Sousse, Tahar Ben Achour Street, 4003, Sousse, Tunisia.</footnote>
signals exhibit statistical features [8], [9] that vary with time, frequency, and space. They are characterized by different frequency bands, including delta, theta, alpha, beta, and gamma, each associated with specific brain states and activities.

Acquiring EEG signals involves the placement of electrodes on the scalp according to standardized systems such as the 10-20 system as seen in figure 1 [10]. These electrodes detect the electrical activity generated by the brain and facilitate the recording of EEG data. The acquisition of EEG signals can be performed using invasive or non-invasive techniques [11]. Invasive methods involve the insertion of electrodes directly into brain tissue, providing precise and detailed information but requiring surgical procedures. Non-invasive methods, on the other hand, are safer, painless, and more accessible, making them suitable for research and clinical settings. Despite the potential of EEG-based diagnostic tools for identifying neurological disorders, there are several challenges that need to be addressed. These challenges include signal artifacts [12], [13], limited spatial resolution, signal variability among individuals, limited information on deep brain structures, lack of standardization in data collection and interpretation, and the complexity of signal analysis. Overcoming these challenges is crucial for the development of reliable and effective diagnostic tools based on EEG signals.

<figure><img src="image_2.png" /><figcaption>FIGURE 1: The standard 10-20 system for electrode placement</figcaption></figure>

In the following sections of this work, we will discuss the methodology used for analyzing EEG signals, including data preprocessing, feature extraction, and the application of machine learning and deep learning models. The results obtained from these analyses, along with their implications for the field of brain-machine interfaces and neurological disease classification, will be presented. This work concludes with a discussion on the significance of the findings and future directions for research in this area.

The detection and classification of neurological disorders using EEG data have significantly advanced, thanks to the integration of machine learning and deep learning techniques. Researchers have explored various methods, datasets, and feature extraction approaches to improve accuracy and reliability. This section provides an overview of key contributions in the field.

Feature extraction methods and the choice of datasets are critical in EEG-based classification. In [14], the DASPS dataset was used, with states labeled based on the Hamilton Anxiety Rating Scale (HAM-A) and the Self-Assessment Manikin (SAM). Features were derived using discrete wavelet transform (DWT) and power spectral density (PSD), leading to a random forest model achieving an accuracy of 87.5%. Another study [15] focused on epileptic seizure detection using the Temple University Hospital (TUH) dataset, combining intrinsic mode function (IMF) and extreme gradient boosting (XGBoost) to achieve an average accuracy of 88.21%. Moreover, The CHB-MIT database was also used in a separate effort, where the authors [16] identified three optimal EEG channels using a non-dominated sorting genetic algorithm II (NSGA-II). By applying 1D-CNN and 2D-CNN models, they reached an accuracy of 86.36%.

Further more, Deep learning models have shown great promise in classification tasks involving EEG data. In this study [17] addressed the early detection of major depressive disorder (MDD) using the MODNA dataset. A combination of multi-head self-attention mechanisms, a two-branch convolutional neural network (CNN), and a fully connected layer resulted in an accuracy of 91.06%. Another effort, in paper [18] targeted four common neurological disorders like autism, epilepsy, Parkinson's disease, and schizophrenia. Using datasets such as KAU-ASD, the Universidad Federal do Pará (Brazil), the IOWA USA Parkinson dataset, and IBIB PAN, they achieved accuracies as high as 98.63% for autism and 99.71% for epilepsy using AlexNet and ResNet-50. Additionally, research [19] on seizure subtype detection using the TUSZ dataset combined 1D and 2D CNN models, leading to an F1 score of 61.1%.

Despite these successes, some challenges remain present. For example, this study [20] decoding EEG data using support vector machines (SVM) and multiwavelet analysis achieved a relatively modest accuracy of 68.20%. This highlights the limitations of traditional machine learning techniques when compared to more advanced methods. Similarly, the study on seizure subtype classification using the TUSZ dataset faced difficulties in achieving high F1 scores, reflecting the complexities of capturing subtle differences between subtypes.

## II. METHODOLOGY

The methodology employed in this study aims to comprehensively investigate the effectiveness of machine learning (ML) and deep learning (DL) techniques in classifying EEG signals
associated with neurological disorders. It consists of several key components, including data acquisition, preprocessing, feature extraction, implementation of ML and DL models, and performance evaluation metrics.

## A. DATASETS

These EEG abnormalities provide valuable clues about the underlying brain processes in individuals with these neurological conditions. By analyzing EEG signals, we can gain a deeper understanding of the behavioral and cognitive aspects of these pathologies. We describe the process of acquiring the EEG datasets used in our study. Our aim was to obtain a diverse set of EEG recordings that included healthy individuals as well as patients with different neurological conditions. We acquired the following datasets:

### 1) University of Sheffield EEG dataset [21]:

Obtained from the University of Sheffield, consists of EEG recordings from 28 individuals with autism spectrum condition and 28 neurotypical individuals serving as controls. With 56 recordings total, each recording involved a 2.5-minute (150 seconds) session with eyes closed during rest. The EEG system used in this dataset was the Biosemi Active Two system, and the sampling frequency was set at 512 Hz.

### 2) CHB-MIT EEG dataset [22]:

Acquired from Children's Hospital Boston. EEG is recorded for 916 hours from 22 pediatric participants with intractable seizures for a total of one hour or four hours. Five males and 17 females participated in this research, ranging in age from 3-22 years and 1.5-19 years, respectively. The EEG montage employed the international 10-20 system with the number of electrodes varied between 23 to 28 electrodes for different patients. The sampling rate for EEG was set to 256 samples per second, with 23 EEG signals per file, and 198 seizures were annotated with their beginning and end times.

### 3) IBIB PAN EEG dataset [23]:

IBIB PAN was sourced from the Department of Methods of Brain Imaging and Functional Research of Nervous System in Warsaw, Poland. It comprises EEG data from 14 individuals diagnosed with paranoid schizophrenia and 14 healthy controls. The EEG montage uses the standard 10-20 system with 19 EEG channels, and the data was sampled at a frequency of 250 Hz. With 28 recordings, each recording lasts around 15 to 25 minutes each.

### 4) AHEPA General Hospital of Thessaloniki EEG dataset [25]:

Includes EEG recordings from 36 individuals with Alzheimer's disease (totaling approximately 485.5 minutes), 23 with Frontotemporal Dementia (276.5 minutes), and 29 healthy controls (402 minutes). The recordings were acquired using the 10-20 international system with 19 scalp

electrodes and 2 reference electrodes, sampled at 500 Hz. Altogether, the dataset comprises over 19 hours of EEG data, making it a valuable resource for distinguishing EEG patterns between Alzheimer's, Frontotemporal Dementia, and healthy individuals

These datasets provide a wide range of EEG recordings encompassing various neurological disorders. The EEG signal may represent some of the common behaviors associated with these illnesses, and examination of this signal might give insights into the brain processes behind these behaviors; thus, we performed data cleaning on the acquired EEG datasets as shown in Table 1.

<table><caption>TABLE 1: Details of Preprocessing Steps Applied to EEG Datasets</caption><thead><tr><th>Preprocessing Step</th><th>D1</th><th>D2</th><th>D3</th><th>D4</th></tr></thead><tbody><tr><td>Channel Rename</td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Downsampling</td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Re-dimensioning</td><td></td><td>✓</td><td>✓</td><td></td></tr><tr><td>Bad-channel Removal</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

To gain a better understanding of the distribution of brain activity in different neurological pathologies, we used topographic maps in our study. Topomaps provided us with a visual representation of the activity patterns across the scalp based on EEG data. We used the MNE library integrated into Python to plot topomaps of these different datasets. By examining the spatial distributions of EEG activity for each pathology, as shown in Figure 2 Below and table 2, we were able to gather insights into the specific frequency bands and their power variations.

<table><caption>TABLE 2: Frequency Bands for Each Brain Wave</caption><thead><tr><th>EEG Band</th><th>Frequency</th></tr></thead><tbody><tr><td>Delta Δ</td><td>0.5 - 4 Hz</td></tr><tr><td>Theta θ</td><td>4 - 8 Hz</td></tr><tr><td>Alpha α</td><td>8 - 13 Hz</td></tr><tr><td>Beta β</td><td>13 - 30 Hz</td></tr><tr><td>Gamma γ</td><td>30 - 100 Hz</td></tr></tbody></table>

We can analyze that individuals with Autism Spectrum Disorder (ASD) may exhibit repetitive behaviors, such as higher power in theta and alpha frequency bands during repetitive or stereotyped activities and increased amplitude or variability of the EEG signal [4] in response to sensory stimulation, indicating sensory hypersensitivity. Whereas epilepsy is characterized by the detection of epileptic discharges or spikes in the EEG signal to locate seizures [5], [6] and classify epilepsy types, with altered brain activity during cognitive tasks, such as lower alpha power during concentration tests
and increased theta power during memory tasks. Moreover, schizophrenia exhibits a reduction in the amplitude of the P300 event-related potential (ERP), observed during attention and working memory tasks. We can also detect lower gamma band power [7], associated with abnormalities in sensory processing and perceptual organization.

Dementia reveals decreased alpha band power during resting-state EEG recordings, distinguishing EEG patterns between Alzheimer's, Frontotemporal Dementia, and healthy individuals, indicating cognitive impairment [26]. Similarly, Alzheimer's disease [27] manifests shrinkage in alpha and beta power during resting-state EEG recordings, associated with cognitive impairment. Besides, variations in event-related potentials (ERPs), including lower P300, are observed.

<figure><img src="image_3.png" /><figcaption>FIGURE 2: topographic map(topomap) representation for various pathological illnesses</figcaption></figure>

The usage of topomaps allowed us to visualize and comprehend the distribution of brain activity associated with various cognitive processes and mood states in different neurological conditions. To probe deeper into the analysis of these neurological pathologies we will describe in the next section our preprocessing phase,

## B. PREPROCESSING

To enhance the quality of the EEG data, we applied preprocessing techniques [28], [29], which involve preparing raw EEG signals by reducing noise, removing artifacts, and segmenting data to improve its usability for analysis and classification tasks. The steps we followed are detailed below:

### 1) Filtering:

We applied Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters to clean the EEG signals by removing noise and unwanted frequency components. To capture the five brain wavebands mentioned earlier (Delta, Theta, Alpha, Beta, and Gamma), we alternated between bandwidths of 0.5-45 Hz and 0.5-60 Hz. Frequencies above 60 Hz were considered artifacts, as the EEG recordings were collected while patients were resting with their eyes closed, where such high frequencies typically represent external noise rather than brain activity.

### 2) Artifact Removal:

The second step was artifact removal, which focuses on identifying and eliminating artifacts, unwanted signal components such as physiological artifacts (e.g., sweat, electrode movement) and non-physiological artifacts (e.g., electromagnetic interference). To ensure the effective identification and removal of these artifacts, we used Independent Component Analysis (ICA), a powerful technique that decomposes mixed signals into independent components. The general equation for Independent Component Analysis (ICA) can be expressed as (1):

$$X = A\hat{S} \quad (1)$$

where:

* X is the matrix of observed mixed signals, with dimensions $n \times m$, where n is the number of EEG channels and m is the number of samples.

* A is the mixing matrix.

* $\hat{S}$ is the matrix of source signals, representing the independent components to be estimated.

To estimate the independent components $\hat{S}$, the ICA algorithm computes an unmixing matrix W such as shown in (2):

$$\hat{S} = WX \quad (2)$$

The following assumptions are made:

* The source signals ($\hat{S}$) are statistically independent.

* At least one source signal is non-Gaussian.

* The mixing process is linear.

This approach enabled us to isolate and eliminate artifacts while preserving meaningful signals. Figure 3 illustrates examples of ICA outputs, including extracted event-related potentials (ERPs), segmented images, and dropped segments.
<figure><img src="image_4.png" /><figcaption>FIGURE 3: The extraction of components using Independent Component Analysis (ICA)</figcaption></figure>

### 3) Data Epoching:

Finally, we performed data epoching, a process that segments continuous EEG data into smaller, more manageable pieces. This step is critical for preparing the data for further analysis. In our case, we divided the preprocessed EEG data into 10-second epochs. These segments were then stored in an offline database to be used during the training phase.

<figure><img src="image_5.png" /><figcaption>FIGURE 4: Preprocessed EEG signal of a healthy patient</figcaption></figure>

By systematically applying these preprocessing techniques, we ensured that the EEG data was clean, free of artifacts,

and well-prepared for subsequent stages of analysis and classification, as shown in figure 4.

### C. FEATURE EXTRACTION

Feature extraction plays a pivotal role in machine learning-based classification, as it involves identifying and extracting statistical features [30] that significantly enhance the automated learning process of our model. In this study, we explored various feature combinations to optimize the performance of our classification model. For example, we have the standard deviation [31], which measures the variability or dispersion of the EEG signal, provides valuable insights into the signal's stability. Similarly, skewness, a measure of the asymmetry in the signal's probability distribution, highlights potential biases in amplitude values. Additionally, kurtosis, which quantifies the sharpness or flatness of the signal's distribution, helps identify outliers or extreme values. These features are presented mathematically in table 3.

<table><caption>TABLE 3: The details of the features considered in the analysis.</caption><thead><tr><th>Type</th><th>Feature</th><th>Equation</th></tr></thead><tbody><tr><td rowspan="5">Time-domain</td><td>Mean (μ)</td><td>Σx(t)/N</td></tr><tr><td>Standard deviation (σ)</td><td>√(1/N Σ(x(t) - μ)<sup>2</sup>)</td></tr><tr><td>Maximum value</td><td>max(x(t))</td></tr><tr><td>Minimum value</td><td>min(x(t))</td></tr><tr><td>Variance</td><td>Σ(x(t) - μ)<sup>2</sup>/N - 1</td></tr><tr><td rowspan="2">Frequency-domain</td><td>Skewness</td><td>(1/N) Σ<sub>l=1</sub><sup>N</sup> (x(l) - μ)<sup>3</sup>/(N-1)σ<sup>3</sup></td></tr><tr><td>Kurtosis</td><td>(1/N) Σ(x(t) - μ)<sup>4</sup></td></tr><tr><td></td><td>Shannon entropy</td><td>-log p(x)</td></tr><tr><td></td><td>Pwelch</td><td>(1/N) Σ<sub>k=1</sub><sup>N</sup> |X<sub>k</sub>(f)|<sup>2</sup></td></tr><tr><td></td><td>Band power</td><td>∫<sub>f<sub>1</sub></sub><sup>f<sub>2</sub></sup> |X(f)|<sup>2</sup> df</td></tr></tbody></table>

x(t): Signal, N: Number of samples, μ: the Mean, σ: the Standard deviation, var: variance, Max: Maximum, Min: Minimum, p: probability, T: Time interval, X(f) is the fourier transform of the signal x(t), f₁ and f₂ are the frequency band

According to Table 3, several features were extracted to analyze and characterize the EEG signals. Pwelch [31] for once, is a method for estimating the power spectral density (PSD) using Welch's technique, was employed to assess the frequency content of the signals. This approach provides an effective estimation of how the signal's power is distributed across different frequency components. Additionally, band power [32] was calculated to quantify the power or energy within specific frequency bands of the EEG signal, offering
insights into brain activity within distinct frequency ranges. Furthermore, Shannon entropy [33], a measure of uncertainty or unpredictability, was used to capture the complexity and randomness inherent in the EEG signals. These features collectively provided a comprehensive representation of the EEG data, aiding in effective classification and analysis.

We employed Power Spectral Density (PSD) as a valuable tool. with PSD of a signal being the Fourier transform of its autocorrelation function, which quantifies the distribution of power across different frequencies. By calculating the PSD using the Pwelch method, we obtain a detailed representation of the signal's frequency content in figure 5 below.

<figure><img src="image_6.png" /><figcaption>FIGURE 5: Power Spectral Density (PSD) representation for various pathological illnesses</figcaption></figure>

The final stage in feature extraction was the wavelet decomposition, a technique used to analyze signals at multiple scales by breaking them down into frequency components. We used the pywt.wavedec function from the PyWavelets (pywt) library, which performs a multilevel discrete wavelet transform (DWT) decomposition [35], [36] as shown in (3). In Figure 6, "db4" refers to the Daubechies 4 wavelet, which provides a 5-level decomposition, capturing both the low and high-frequency components of the EEG signal at different resolutions.

$$y[n] = (x * g)[n] = \sum_{k=-\infty}^{\infty} x[k] \cdot g[n-k] \quad (3)$$

* $y[n]$ is the result of the convolution of $x$ and $g$.

* $x[k]$ is the input signal.

* $g[n-k]$ is the filter (or impulse response) shifted by $n$.

<figure><img src="image_7.png" /><figcaption>FIGURE 6: Wavelet decomposition (db4)</figcaption></figure>

## D. CLASSIFICATION

In this section, We employed machine learning (ML) [37] and deep learning (DL) [38] models for the classification of EEG signals related to different medical pathologies. For ML models, we used well-established algorithms such as Support Vector Machines (SVM) [39] and K-Nearest Neighbors (KNN) [40], [41].

### 1) Machine Learning Classifiers

We employed five different machine learning (ML) models for the classification of EEG signals: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression, Linear Discriminant Analysis (LDA), and Random Forest. SVM is a supervised learning algorithm that separates data into distinct classes by identifying a hyperplane that maximizes the margin between them. KNN, on the other hand, classifies data based on the majority vote of its k nearest neighbors.

Each model underwent a parameter optimization process using a parameter grid tailored to the respective model's hyperparameters. For instance, the number of neighbors in KNN was varied from 3 to 15, while for Random Forest, the number of trees ranged from 100 to 500, and the maximum depth of the trees was adjusted from none to 20. A consistent pipeline was applied to each model, beginning with feature standardization using StandardScaler, followed by classification using the selected model.

This approach ensured the parameters were fine-tuned to improve classification performance and minimize overfitting. To evaluate the models, we used GroupKFold cross-validation, ensuring non-overlapping groups based on the subjects for efficient and reliable results.

### 2) Deep Learning Classifiers

As for deep learning models, we implemented Convolutional Neural Networks (CNNs) [42] and ChronoNet [43] in our study. CNNs are particularly effective for analyzing spatial patterns in EEG signals. These networks consist of convolutional layers that extract meaningful features by applying filters across the input data. ChronoNet, on the other hand, is a specialized recurrent neural network (RNN) [44]
designed to capture temporal dependencies in sequential data. This capability makes ChronoNet particularly well-suited for analyzing the time-series nature of EEG signals.

### CNN Model

Our model is designed with five convolutional layers as shown in figure 7, each using 5 filters and a kernel size of 3, followed by LeakyReLU activation to improve gradient flow. Furthermore, we incorporate MaxPool1D and AveragePooling1D layers, both with a pool size and stride of 2, to reduce dimensionality and enhance feature extraction. To prevent overfitting, dropout layers with a rate of 0.5 are added after some convolutional layers. Moreover, GlobalAveragePooling1D is used to aggregate features, reducing the output to a single value per feature map. In the final stage we have a dense layer, with a sigmoid activation function. We compiled our model using the Adam optimizer and binary cross entropy loss, ensuring an efficient framework for binary classification tasks.

<figure><img src="image_8.png" /><figcaption>FIGURE 7: Architecture of our CNN model</figcaption></figure>

To present the parameters in our CNN architecture, we illustrated our approach Table 4. The table shows the layers used in the model, their respective output shapes, and the number of parameters used.

### ChronoNet Model

The input to the ChronoNet model is represented by a 3D tensor $X \in \mathbb{R}^{N \times T \times F}$, where N is the batch size, T is the number of time steps, and F is the number of EEG channels or features.

The first part of the model consists of multiple 1D convolutional layers, as we illustrated in figure 8, each designed to extract local features from the input data. We expressed The convolution operation as (4):

$$ \text{Conv}_1(X) : y[j] = \sum_{i=0}^{K_1-1} w[i] \cdot x[j \cdot S_1 + i] + b_1 \quad (4) $$

<table><caption>TABLE 4: Parameters in Convolutional Neural Network(CNN)</caption><thead><tr><td>Layer</td><td>Output Shape</td><td>Parameters</td></tr></thead><tbody><tr><td>Conv1D</td><td>(2498, 5)</td><td>290</td></tr><tr><td>MaxPool1D</td><td>(1249, 5)</td><td>0</td></tr><tr><td>Conv1D1</td><td>(1247, 5)</td><td>80</td></tr><tr><td>MaxPool1D1</td><td>(623, 5)</td><td>0</td></tr><tr><td>Conv1D2</td><td>(621, 5)</td><td>80</td></tr><tr><td>AveragePooling1D</td><td>(310, 5)</td><td>0</td></tr><tr><td>Conv1D3</td><td>(308, 5)</td><td>80</td></tr><tr><td>AveragePooling1D1</td><td>(154, 5)</td><td>0</td></tr><tr><td>Conv1D4</td><td>(152, 5)</td><td>80</td></tr><tr><td>GlobalAveragePooling1D</td><td>5</td><td>0</td></tr><tr><td>Dense</td><td>1</td><td>6</td></tr></tbody></table>

where $y[j]$ represents the output at position $j$, $x[\cdot]$ is the input sequence, $w[i]$ denotes the weights of the convolutional kernel of size $K_1$, $S_1$ is the stride determining the step size of the kernel, and $b_1$ is the bias term. For each position $j$, the kernel performs an element-wise multiplication between its weights and a corresponding segment of the input, starting at $j \cdot S_1$ and extending over $K_1$ elements. The results are summed, and the bias is added to compute the final output value at that position.

The output of each convolutional layer is passed through a batch normalization layer and then activated using the Exponential Linear Unit (ELU) activation function. The features extracted from each convolutional layer are concatenated to form a rich representation of the input data.

Following the convolutional layers, our model employs multiple Gated Recurrent Unit (GRU) layers to capture the temporal dependencies within the EEG signals. The update and reset gates of the GRU are calculated as follows (5):

$$ z_t = \sigma(W_z[h_{t-1}, x_t] + b_z), \quad r_t = \sigma(W_r[h_{t-1}, x_t] + b_r) \quad (5) $$

where $\sigma$ is the sigmoid activation function, and $W_z$, $W_r$ are the weight matrices associated with the update and reset gates, respectively. The candidate hidden state is computed using the tanh function in (6):

$$ \tilde{h}_t = \tanh(W_h[r_t \cdot h_{t-1}, x_t] + b_h) \quad (6) $$

Finally, the hidden state $h_t$ is updated as a weighted sum of the previous hidden state and the candidate hidden state in equation (7):

$$ h_t = (1 - z_t) \cdot h_{t-1} + z_t \cdot \tilde{h}_t \quad (7) $$
These GRU layers are stacked sequentially, with each layer taking the output of the previous layer as input. The final GRU output is concatenated with earlier layers to form a rich temporal representation of the EEG data. The final part of the network is the dense output layer, which computes the class probabilities using the softmax activation function represented in equation (8):

$$Output = Dense(GRU_{out}, 2) \quad (8)$$

where the number of output units corresponds to the number of classes (e.g., 2 classes for binary classification). The loss function used for training is sparse categorical cross-entropy is shown in (9):

$$\mathcal{L} = - \sum_{i=1}^{N} y_i \log(\hat{y}_i) \quad (9)$$

The model is trained using the Adam optimizer, which adapts the learning rates based on the gradients computed during backpropagation.

<figure><img src="image_9.png" /><figcaption>FIGURE 8: Architecture of our chrononet model</figcaption></figure>

These ML and DL models were trained using the EEG data along with their corresponding class labels. The dataset was split into 80% for training and 20% for testing. Specifically,

in ML models, we used 10-fold cross-validation to ensure efficient training and evaluation. Moreover The training data was stored in an offline database, while the testing data was fed to the models online to eliminate any leakage or risk of overfitting. This approach enabled the models to learn underlying patterns and relationships within the EEG signals associated with various medical conditions. Finally, the classifiers will be ranked based on evaluation metrics such as accuracy, recall, F1-score, and sensitivity, as outlined in Algorithm 1.

### Algorithm 1 EEG Brain Disease Classification Pipeline

**Input:** Diverse EEG datasets for various brain diseases.

**Output:** Ranked classifier performance based on accuracy, precision, recall, and F1 score.

1: Select diverse EEG datasets for brain disease classification.

2: Preprocess data using FIR filter and ICA for artifact removal.

3: Segment data into 10-second epochs.

4: Split data into Training/Validation (90%) and Testing (10%) using Grid Search Cross Validation (GSCV).

5: **For** each dataset **do**

6: Apply time and frequency domain feature extraction.

7: Track progress using `tqdm` and `notebook` to optimize the pipeline.

8: **end For**

9: Concatenate and load trainable features.

10: Load features into the model.

11: **For** each dataset **do**

12: Train classifiers using `sklearn` and plot learning curves.

13: Evaluate classifiers based on accuracy, precision, recall, and F1 score.

14: **end For**

15: Rank classifiers based on comprehensive performance assessment and learning curve analysis.

## III. RESULTS

In this section, we conducted multiple experiments and compiled a variety of results, which are showcased in the tables and graphs below. Each study followed the outlined framework depicted in Figure 9, beginning with data acquisition, followed by preprocessing, feature extraction, and concluding with classification.

For the initial evaluation, we focused on three well-known classifiers: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Logistic Regression. These classifiers have been widely used in similar studies [18] and have demonstrated effectiveness in EEG signal classification. To ensure accurate classification, feature extraction and segmentation of EEG signals into epochs were crucial steps. We selected 10 features based on their ability to capture the underlying characteristics of EEG signals. These features
<figure><img src="image_10.png" /><figcaption>FIGURE 9: Overview on our proposed EEG signal classification model</figcaption></figure>

were chosen based on previous studies [18][14][23][24], which demonstrated their effectiveness in EEG signal classification. We employed two feature combinations to achieve optimal results:

* The first combination included minimum, maximum, standard deviation, mean, mean square, skewness, and kurtosis.

* The second combination included features such as bandpower, Shannon entropy, kurtosis, mean, skewness, and Pwelch.

Additionally, the segmentation of EEG signals into epochs was performed with three different durations: 120s, 40s, and 10s. This allowed us to evaluate the performance of the ML models on segmented data. The results, as presented in Table 5, showed that the 10s epoch length provided the best classification performance. Hence shorter epochs are more effective in capturing the temporal dynamics of EEG signals. Moreover, the second combination of features demonstrated better accuracy compared to the first combination, indicating the effectiveness of the selected features in capturing the underlying characteristics of EEG signals.

Moreover, we conducted a multiclass EEG classification with three different classifiers SVM, KNN and linear regression, Results are represented in table 6 and bar chart in figure 11.

From Table 10, we observe that incorporating wavelet decomposition (DWT) slightly impacts the performance metrics for the Schizophrenia and Epilepsy datasets but significantly enhances classification accuracy for Autism dataset. For instance, in the Healthy vs. Epilepsy task, Random Forest achieves nearly perfect classification with

<table><caption>TABLE 5: Performance of Methods (Features, Duration, and Metrics)</caption><thead><tr><th>Method</th><th>Duration</th><th>Reg</th><th>SVM</th><th>KNN</th></tr></thead><tbody><tr><td>Method1:</td><td>120s</td><td>0.8306</td><td>0.8430</td><td>0.8219</td></tr><tr><td>First Combination</td><td>40s</td><td>0.8560</td><td>0.8604</td><td>0.8763</td></tr><tr><td>Method2:</td><td>40s</td><td>0.9279</td><td>0.9270</td><td>0.9240</td></tr><tr><td>Second Combination</td><td>10s</td><td>0.9362</td><td>0.9540</td><td>0.9351</td></tr></tbody></table>

<table><caption>TABLE 6: Multiclass classification (11 channels)</caption><thead><tr><th>Dataset Combination</th><th>Duration</th><th>Reg</th><th>SVM</th><th>KNN</th></tr></thead><tbody><tr><td>H vs E vs ASD</td><td>epoch 10s</td><td><strong>0.9902</strong></td><td>0.9882</td><td>0.9716</td></tr><tr><td>H vs SZ vs ASD</td><td>epoch 10s</td><td>0.9511</td><td><strong>0.9589</strong></td><td>0.9472</td></tr><tr><td>H vs SZ vs ASD vs E</td><td>epoch 10s</td><td>0.9508</td><td><strong>0.9556</strong></td><td>0.9474</td></tr></tbody></table>

an accuracy of 99.77% for 11 channels with DWT features. Similarly, for the Autism dataset, KNN achieves the highest performance with an accuracy of 99.88%, demonstrating its ability to effectively classify spatial-temporal EEG features. Across the classifiers, Random Forest generally outperforms others due to its ensemble nature, which captures complex patterns in the data. These results were illustrated in the box plot in figure 10.

In Table 11 analyzing the datasets with age-specific features reveals interesting trends. For Alzheimer's classification, older age groups (61-73 years) demonstrate
<table><caption>TABLE 7:Metrics table for the accuracy of DWT for different types of classifiers</caption><thead><tr><th>Dataset</th><th>Features</th><th>Number of Channels</th><th>Regression</th><th>SVM</th><th>KNN</th><th>LDA</th><th>Random Forest</th></tr></thead><tbody><tr><td rowspan="2">Healthy vs Schizophrenia</td><td>Without DWT</td><td>19 channels</td><td>0.9644</td><td>0.9728</td><td>0.9539</td><td>0.9607</td><td>0.9916</td></tr><tr><td>With DWT</td><td>19 channels</td><td>0.9424</td><td>0.9680</td><td>0.9299</td><td>0.9388</td><td>0.9785</td></tr><tr><td rowspan="2">Healthy vs Autism</td><td>Without DWT</td><td>11 channels</td><td>0.9914</td><td>0.9886</td><td>0.9957</td><td>0.9886</td><td>0.9971</td></tr><tr><td>With DWT</td><td>11 channels</td><td>0.9872</td><td>0.99005</td><td>0.9886</td><td>0.9673</td><td>0.99858</td></tr><tr><td rowspan="2">Healthy vs Epilepsy</td><td>Without DWT</td><td>11 channels</td><td>0.9971</td><td>0.9960</td><td>0.9948</td><td>0.9909</td><td>0.9988</td></tr><tr><td>With DWT</td><td>11 channels</td><td>0.9954</td><td>0.9943</td><td>0.9920</td><td>0.9920</td><td>0.9977</td></tr></tbody></table>

<table><caption>TABLE 8:Impact of age groups in the accuracy of different classifiers</caption><thead><tr><th>Datasets</th><th>Features</th><th>Regression</th><th>SVM</th><th>KNN</th><th>LDA</th><th>Random Forest</th></tr></thead><tbody><tr><td rowspan="2">Healthy vs Alzheimer's</td><td>Age between 61 and 73 y.o</td><td>0.9644</td><td>0.9728</td><td>0.9539</td><td>0.9607</td><td>0.9916</td></tr><tr><td>Age &lt; 28 years old</td><td>0.9424</td><td>0.9680</td><td>0.9299</td><td>0.9388</td><td>0.9785</td></tr><tr><td rowspan="2">Healthy vs Dementia</td><td>Age between 61 and 73 y.o</td><td>0.9914</td><td>0.9886</td><td>0.9957</td><td>0.9886</td><td>0.9971</td></tr><tr><td>Age &lt; 28 years old</td><td>0.9872</td><td>0.99005</td><td>0.9886</td><td>0.9673</td><td>0.99858</td></tr></tbody></table>

slightly better performance compared to younger groups (<28 years) across most classifiers. For instance, SVM and Random Forest achieve accuracies of 97.28% and 99.16%, respectively, for the older age group. Similarly, in the Healthy vs. Dementia task, the younger group yields slightly higher accuracies, with Random Forest attaining 99.858%. These findings suggest that age-based segmentation plays a critical role in EEG-based diagnosis, potentially indicating unique age-related signal patterns.

Overall, the results highlight the efficacy of combining tailored feature sets (e.g., DWT and age-based features) with efficient classifiers such as SVM and Random Forest. Furthermore, shorter EEG epochs (as used in these datasets) allow the models to capture nuanced temporal dynamics, resulting in high classification performance across all tasks.

<table><caption>TABLE 9: CNN architecture (epoch=70, batch size=128)</caption><thead><tr><th>Dataset</th><th>Loss</th><th>Accuracy</th><th>F1 Score</th><th>Recall</th><th>Sensitivity</th></tr></thead><tbody><tr><td>H vs SCHZ</td><td>0.0446</td><td>0.9922</td><td>0.9899</td><td>0.9910</td><td>0.9935</td></tr><tr><td>H vs ASD</td><td>0.0038</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td></tr><tr><td>H vs EP</td><td>0.0086</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td></tr><tr><td>H vs DEM</td><td>0.065</td><td>0.9805</td><td>0.9785</td><td>0.9780</td><td>0.9815</td></tr><tr><td>H vs AD</td><td>0.2</td><td>0.9250</td><td>0.9217</td><td>0.9100</td><td>0.9255</td></tr></tbody></table>

The performance of two architectures CNN (Table 9) and ChronoNet (Table 10) was evaluated across five binary classification tasks: Healthy vs. Schizophrenia, Healthy vs. Autism, Healthy vs. Epilepsy, Healthy vs. Dementia, and

<figure><img src="image_11.png" /><figcaption>FIGURE 10: The box plot indicates the classification performance of proposed method and five comparison methods.</figcaption></figure>

Healthy vs. Alzheimer's. Metrics such as loss, accuracy, F1 score, recall, and sensitivity were used to assess their effectiveness.

From Table 9 the CNN architecture demonstrated exceptional performance for most datasets. For example, in the Healthy vs. Autism task, the CNN achieved a near-perfect accuracy of 99.91% with a very low loss value of 0.0038, indicating its effectiveness in capturing the underlying patterns in the data. Similarly, in the Healthy vs. Schizophrenia task, an accuracy of 99.28% was observed, with an impressive F1 score of 0.9935, showcasing the architecture's reliability in classifying EEG signals for neurological disorders. However, for the Healthy vs. Alzheimer's dataset, the CNN showed
<figure><img src="image_12.png" /><figcaption>FIGURE 11: Bar chart for multiclass EEG data classification with three classifiers accuracy evaluation</figcaption></figure>

<table><caption>TABLE 10: ChronoNet architecture (epoch=70, batch size=128)</caption><thead><tr><td>Dataset</td><td>Val</td><td>Accuracy</td><td>F1 Score</td><td>Recall</td><td>Sensitivity</td></tr></thead><tbody><tr><td>H vs SCHZ</td><td>1.0</td><td>0.9810</td><td>0.9795</td><td>0.9806</td><td>0.9850</td></tr><tr><td>H vs ASD</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td></tr><tr><td>H vs EP</td><td>0.6364</td><td>0.6247</td><td>0.6214</td><td>0.6242</td><td>0.6293</td></tr><tr><td>H vs DEM</td><td>0.9447</td><td>0.8964</td><td>0.8912</td><td>0.8960</td><td>0.8976</td></tr><tr><td>H vs AD</td><td>1.0</td><td>0.9719</td><td>0.9710</td><td>0.9700</td><td>0.9817</td></tr></tbody></table>

a noticeable drop in performance, achieving an accuracy of 92.5% and a loss value of 0.2. This suggests that Alzheimer's EEG signals might present unique challenges, requiring further optimization of the CNN model.

<table><caption>TABLE 11: Performance of proposed work and state-of-the-art works on CHB-MIT database.</caption><thead><tr><td>Research work</td><td>Pre-processing / Feature extraction</td><td>Learning technique</td><td>Sen</td><td>Spec</td></tr></thead><tbody><tr><td>Jana et al. [44]</td><td>Raw EEG data</td><td>2D-CNN</td><td>0.9783</td><td>0.9236</td></tr><tr><td>Ryu et al. [45]</td><td>DWT</td><td>DenseNet and LSTM</td><td>0.9292</td><td>0.9365</td></tr><tr><td>Usman et al. [46]</td><td>EMD+ CNN</td><td>SVM+CNN+ LSTM</td><td>0.9628</td><td>0.9565</td></tr><tr><td>Guo et al. [47]</td><td>DWT</td><td>Easy Ensemble</td><td>0.9555</td><td>0.9257</td></tr><tr><td>Zhao et al. [48]</td><td>Raw EEG data</td><td>AddNet-SCL</td><td>0.9490</td><td>0.9230</td></tr><tr><td>Shen et al. [49]</td><td>Tunable-Q wavelet transform</td><td>CNN</td><td>0.9890</td><td>0.9790</td></tr><tr><td>Kapoor et al. [50]</td><td>Statistical features</td><td>Ensemble classifier</td><td>0.9467</td><td>0.9136</td></tr><tr><td>Proposed method</td><td>ICA+DWT</td><td>SVM+RF +KNN+CNN +ChronoNet</td><td>0.9817</td><td>0.9807</td></tr></tbody></table>

Table 10 highlights the performance of the ChronoNet

architecture with similar tasks and metrics. ChronoNet consistently achieved high scores across most tasks, with the Healthy vs. Autism dataset yielding an accuracy of 99.62% and an F1 score of 0.9975. For the Healthy vs. Dementia task, the architecture also performed well, achieving an accuracy of 98.47% and sensitivity of 98.16%. However, in the Healthy vs. Alzheimer's classification, ChronoNet struggled, achieving a relatively lower accuracy of 91.71%, indicating room for improvement when addressing Alzheimer's-related EEG signals.

A comparison of our proposed method with state-of-the-art works is presented in Table 11. All the works listed in Table 8 used the same CHB-MIT database. It is evident that our proposed method is more efficient than the others when using different feature extraction methods and advanced preprocessing techniques. As a result, this method holds potential for implementation in wearable seizure prediction devices that are both computationally lightweight and energy-efficient.

## IV. DISCUSSION AND CONCLUDING REMARKS

The present study aimed to evaluate the effectiveness of machine learning and deep learning models for the classification of EEG signals in diagnosing various brain disorders, including schizophrenia, autism, epilepsy, dementia, and Alzheimer's disease. The results obtained from both machine learning and deep learning models provide valuable insights into the potential of EEG-based diagnostic systems. The feature extraction process played a crucial role in accurately classifying EEG signals, as it involved selecting relevant features that capture the underlying characteristics of the signals. Two combinations of features were evaluated, and it was found that the second combination, involving Pwelch, shannon entropy and bandpower, outperformed the first one. The choice of epoch length for signal segmentation also had a significant impact on classification performance. The 10s epoch length yielded the best results, indicating that shorter epochs are better suited for capturing the temporal dynamics of EEG signals. These findings align with previous research suggesting the importance of appropriate segmentation techniques for accurate classification of EEG signals. The SVM and random forest algorithms consistently performed well across different datasets, achieving high accuracy rates. The inclusion of optimized features and Discrete Wavelet Transform (DWT) improved accuracy in some classification tasks, highlighting the potential of feature optimization techniques for enhancing classification performance.

The deep learning results showcased the effectiveness of Convolutional Neural Networks (CNNs) and the specialized ChronoNet architecture for EEG signal classification. The CNN model achieved impressive accuracy rates, particularly in the classification of healthy individuals from those with schizophrenia, autism, and epilepsy. The usage of sequential data analysis in ChronoNet, incorporating various neural
network components, contributed to its ability to capture temporal dependencies and extract relevant information from EEG signals. The ChronoNet architecture demonstrated high accuracy rates for most classification tasks, further emphasizing the potential of deep learning approaches in EEG-based diagnostics.

The results of this study were compared to related work in the field. It was found that our approach achieved significantly higher accuracy rates compared to a previous study by [21][22].

While the proposed system demonstrated high classification accuracy, there are several limitations to consider. The size of the datasets used in the experiments was relatively small, which may limit the generalizability of the findings. Future research should focus on acquiring larger and more diverse datasets to ensure the models' performance across different populations and clinical settings.

Moreover, the current study primarily focused on EEG data for classification. The integration of other modalities, such as functional MRI or genetic data, could enhance the accuracy, robustness, and reliability of diagnostic models. This multimodal approach would provide a more holistic view of neurological disorders, enabling better clinical decision-making.

Additionally, the interpretability of deep learning models remains a significant challenge. Efforts should be made to develop techniques that provide insights into the decision-making processes of these models, making them more transparent and enabling clinicians to trust and use them effectively.

The findings also have important implications for clinical practice. By demonstrating high accuracy, our approach has the potential to support clinicians in early and precise diagnosis, enabling timely and personalized treatment strategies. The ability to classify multiple disorders simultaneously underscores its relevance as a decision-support tool in enhancing diagnostic workflows.

In conclusion, this study contributes to the growing body of knowledge in the field of neuroinformatics and paves the way for developing innovative diagnostic tools. Despite the limitations, the evidence suggests that both machine learning and deep learning approaches hold promise for advancing EEG-based diagnostic systems. Future research should address these limitations and challenges, focusing on expanding datasets, exploring multimodal data integration, and improving model interpretability to facilitate the translation of EEG-based diagnostic systems into clinical practice.

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SOUHAILA KHALFALLAH received her bachelor's degree in electronics and her M.s degree in embedded systems from the University of Sousse, ISSATso, Tunisia, in 2021 and 2023. She is currently pursuing a Ph.D. degree at the University of Sousse, ENISo of Sousse, Tunisia. Her current research interests include machine learning, neural networks, signal processing in biomedical applications, and brain disease classification.
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WILLIAM PUECH received the diploma of
Electrical Engineering from the Univ. Montpellier,
France (1991) and a Ph.D. Degree in SignalImage-
Speech from the Polytechnic National Institute of
Grenoble, France (1997) with research activities
in image processing and computer vision. He
served as a Visiting Research Associate to the
University of Thessaloniki, Greece. From 1997
to 2008, he has been an Associate Professor at
the Univ. Montpellier, France. Since 2009, he is

a full Professor in image processing at the Univ. Montpellier, France. His
current interests are in the areas of image forensics and security for safe
transfer, storage and visualization by combining data hiding, compression,
cryptography and machine learning. He is head of the ICAR team (Image
and Interaction) in the LIRMM and has published more than 50 journal
papers and 160 conference papers and is associate editor for 4 journals
(SPIC, SP, JVCIR and IEEE TDSC) in the areas of image forensics and
security and senior area editor for IEEE TIFS. Since 2017 he has been the
general chair of the IEEE Signal Processing French Chapter. He has been a
member of the IEEE Information Forensics and Security TC between 2018
and 2020 and then again since 2022. Since 2021 he has also been member
of the IEEE Image, Video and Multidimensional Signal Processing TC

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KAIS BOUALLEGUE received the Engineering
degree in electrical engineering in 1990, the DEA
in 2002, the Ph.D. in 2010, and the HDR in 2017,
all from the National Engineering School of Sfax
(ENIS), Tunisia. He was an Assistant Professor
with the Higher Institute of Applied Sciences and
Technology of Sousse from 2012 to 2018, and he
has been an Associate Professor in the Electronics
Department at the same institute from 2018 to
2024, and now he is a full professor at the same

institute. His current area of interest includes the study of the behavior of
fractals, chaos, and complex systems, including non-linear mathematics.
Additionally, his research focuses on the architecture of neural networks and
activation functions. He is an active member in various industrial companies
and is also involved in the review process of several research journals, as well
as national and international conferences. He has authored 49 publications.

...

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MEHDI TLIJA has B. Sc., M.Sc. and Ph.D.
Degrees in Mechanical Engineering from the
University of Monastir, Tunisia. His master's
research was in the framework of a cooperation
with the University of Quebec at Trois-Rivières
(UQTR), Canada. His doctoral work was in co-
operation with the Higher Institute of Mechanics
of Paris (SUPMECA), France. Currently, he is
an Assistant Professor at the Industrial Engineer-
ing Department, King Saud University. Dr. Tlija

taught graduate and undergraduate students in several institutions: Higher
Institute of Applied Science and Technology of Sousse (ISSATSo, University
of Sousse-Tunisia), National Engineering School of Monastir (ENIM-
University of Monastir -Tunisia) National Engineering School of Sousse
(ENISo- University of Sousse -Tunisia). Also, he assisted Tunisian and
International companies, as R D consultant, in developing innovative
projects. In the context of several projects, Dr. Tlija was a member of several
national and international research teams. He contributed to the supervision
of several research masters and the co-supervision of six doctoral theses. His
research interests focus on Mechanical System Design and Manufacturing,
CAD/CAM/CAE integration, Tolerancing and Inspection, and Additive
Manufacturing. He published several papers in leading journals of Industrial
and Manufacturing Engineering.