# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 3 Datasets and Experimental Results  
The aim of this study is to investigate whether EEG data, when distributed across multiple sources, can be effectively utilized for classification using a dynamic ensemble selection approach. The experiments were conducted on two datasets. The EEG Epilepsy dataset [23] contains exemplary segmented EEG time series recordings from ten epilepsy patients, collected at the Neurology & Sleep Centre in Hauz Khas, New Delhi. Data acquisition was performed using the Grass Telefactor Comet AS40 Amplification System, (manufactured by Grass Technologies, West Warwick, RI, USA), with a sampling frequency of 200 Hz. During the recordings, gold-plated scalp EEG electrodes were placed according to the standard 10-20 system. Although the publicly available dataset contains data for only one channel, the EEG signals were band-pass filtered between 0.5 and 70 Hz and then segmented into three seizure-related stages: pre-ictal, inter-ictal, and ictal. Each single-channel EEG segment was extracted from continuous recordings of multiple patients, and each segment was selected from different channels. Each downloadable folder corresponds to one of these seizure stages and contains fifty MATLAB version 2022a.mat files. Each file includes a 1024-sample EEG time series segment, representing a 5.12 s duration. From the raw signals, 33 features were extracted per segment, in accordance with the feature extraction protocol described in [14]. These features include the following:  
* Statistical features: mean, variance, skewness, kurtosis, root mean square (RMS), and zero crossings. * Spectral power features: delta, theta, alpha, beta, gamma, and total power. * Wavelet-based features: for decomposition levels 0 to 4, the mean, variance, skewness, and kurtosis of the wavelet coefficients were computed. As was mentioned, each observation is a time series of 5.12 s at a frequency of 200 Hz, which gives 1024 points in time, and based on them, the time parameters for the object are calculated (one mat file). As a result, we obtain 150 objects in total. Although the data were originally provided as a single dataset, our objective was to evaluate the performance of a classification system based on distributed data sources, simulating scenarios where EEG data are decentralized. To this end, the original dataset was first split into two parts: a training set containing 120 observations and a test set of 30 observations. For the Epilepsy dataset, the classification was made for three classes--pre-ictal, ictal and post-ictal. The BCI Competition IV Dataset 1 [24] contains EEG recordings from seven subjects performing motor imagery tasks (e.g., left hand, right hand, or foot) with both calibration and evaluation data. Calibration data include visual cues and labels, while evaluation data simulate real-world conditions with auditory cues and no labels. EEG signals were recorded from 59 channels at 1000 Hz. The dataset incorporates both real and artificially generated EEG data to assess the robustness of classification algorithms. The specific nature (real or artificial) of each dataset is undisclosed to participants. This dataset serves as a benchmark for developing and testing algorithms aimed at real-time, asynchronous detection of motor imagery in EEG signals, a critical component for practical BCI applications. The dataset with extracted features for BCI dataset contains 1400 objects with extracted features for each channel. The data were split into training (80%), evaluating (10%), and testing (10%) datasets. The data were split in a stratified way, preserving the relative proportions of each class. Next, the training data were dispersed into multiple independent sources, referred to as local tables, to simulate distributed learning environments. To examine the scalability and robustness of the proposed dynamic ensemble selection model, we experimented with varying numbers of these local data sources. Specifically, we tested configurations with 3, 5, 7, 9, and 11 local tables.