# EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer's Disease
## 1 Introduction  
Alzheimer's diseases (AD) belong to the class of dementia, a neurodegenerative disease characterized by a range of impairments in brain functions, especially memory and learning, as well as executive and motor functions, complex attention, social cognition, and language [1]. The estimated proportion of the general population with dementia is around 50 million of people worldwide, and 60% of those cases correspond to AD [2]. It begins with a symptomatic stage of cognitive decline, called Mild Cognitive Impairment (MCI), characterized by an impairment in cognition that is not severe enough to compromise social and /or occupational functioning [3]. As the progression of this disease lasts for decades, from the appearance of the first sign to the onset of severe clinical symptoms, the clinician's first challenge is to identify the first significant cognitive changes [4]. Indeed, the diagnosis of dementia is usually made when the patient is at least partially dependent on his/her family members [5]. However, a timely diagnosis can facilitate care and support patients and their families in managing this very disabling disease [6]. Conventional techniques to detect AD are costly and distressing. However, electroencephalography (EEG) is a fast, inexpensive, and noninvasive technique to gather brain data, but its interpretation requires a visual inspection, which is often time-consuming and varies with the expertise experience. Moreover, when the EEG recording is long, then its manualreview requires a lot of time with the risk of errors because of the presence of artifacts in the signal. Thus, automated methods based on EEG signal analysis in combination with supervised machine learning have become an important topic of research to assist clinicians in the challenging task of early AD detection [7]. The technique for the sampling of EEG signal consists of placing electrodes on the scalp according to a certain configuration, and the most commonly used is the international 10-20 system [8]. The electrodes record the postsynaptic biopotentials of all the neurons with the same spatial orientation in order to map the electrical activity of the cerebral cortex. The biopotentials, which are sampled in bipolar mode by different electrode pairs or in monopolar mode with a reference electrode, constitute the raw signals. Subsequently, preprocessing procedures clean the raw signals from artefacts and apply band-pass filtering to reject out-of-band noise. Basically, all preprocessing steps convert the raw signals into EEG signals [9]. The composition of the EEG signal is complex, but it can be divided into five frequency bands: delta (1-4 Hz, 8), theta (4-8 Hz, \(\theta\)), alpha (8-13 Hz, \(\alpha\)), beta (13-30 Hz, \(\beta\)), and gamma (30-40 Hz, \(\gamma\)) [10]. Features are extracted from EEG data through a procedure denominated _feature extraction_. Features should be independent and discriminative to facilitate the classification of the subjects. Usually, features such as complexity, coherence, or spectral power are extracted from the time and frequency domains [7]. Fourier transform (FT) is the main technique used to extract frequency domain features for AD detection. Nonetheless, EEG signals are nonstationary and nonlinear in nature. The wavelet transform (WT), which decomposes a signal into the combination of functions (wavelets) of finite length and different frequencies, represents a suitable alternative to address this issue [11]. On the contrary, the frequency domain represents the principal source of the EEG features for AD detection. Indeed, different changes in the frequency patterns of the brain waves have been found in MCI and AD patients compared to healthy aged subjects [12]. All feature extraction methods extrapolate EEG signal features from different domains (e.g., frequency and time) [13]. Then, statistical or machine learning analyses [14] can use these features to develop and validate models based on linear or nonlinear systems [15] to distinguish AD from MCI or normal aging.