# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 1 Introduction
Similarly, in the case of dementia detection, the alteration of brain structure and function which define cognitive impairment might represent a different spatial distribution of load that calls for different rules for choosing the channels [14]. With a direct focus on the channel selection problem in the context of stroke rehabilitation, the proposed study intends to contribute an effective solution that can improve the performance of a BCI and thus improve the rehabilitation process. Channel selection is the main focus of this study. Many electrodes are utilized in medical and diagnostic treatments. For practical BCI applications, many electrodes are needed for classification accuracy; however, putting many electrodes on the scalp is time-consuming and subject to EEG signal overfitting. To fix these issues, researchers need to identify the electrodes that are unable to be classified. Electrode selection can evaluate the neurological knowledge of individuals due to varying reactions and subject-dependent ideal electrode placement [15]. The user-dependent classification job requires automatic electrode relevance determination. This challenge can be overcome by considering a wide range of electrodes and using many methods to identify the appropriate channels for each patient. A particle BA-based optimization technique was used to pick the most accurate EEG channels for stroke patient rehabilitation. Each EEG data channel provides unique classification features. Channel selection reduces data storage and processing, speeds up classifier training by using simpler data, and reduces the \"curse of dimensionality\". Many studies have examined channel selection in healthy people [16; 17], whereas the present study focuses on stroke rehabilitation, where stroke patients' underlying neurophysiological abnormalities and limitations may demand a customized channel selection strategy. Choosing EEG channels for emotion processing, cognitive assessment, and clinical diagnosis often involves knowledge of neural mechanisms and brain regions linked with the desired activities. Common brain activity patterns and optimal channel selection are well established for healthy people. However, strokes can alter brain structure, connectivity, and function. These alterations may cause abnormal brain patterns and compensatory processes [18; 19]. Thus, the best EEG channels for stroke rehabilitation may differ from those recommended for healthy people. To capture stroke patients' neural correlates of motor, cognitive, and behavioral abnormalities, channel selection must be more specialized and personalized due to stroke-induced neuroplasticity. Targeting stroke patients with a customized solution that can properly recognize neural fingerprints and brain activity patterns is intended to connect stroke-related deficiencies. This stage is crucial to developing BCI-based stroke therapies that improve rehabilitation outcomes [20]. The literature on channel selection in healthy people can be useful, but this study recognizes the need to address stroke survivors' unique issues and considerations. Scientists examined channel selection differences between stroke patients and healthy people to better understand neurophysiological changes and optimal BCI setups for stroke rehabilitation. Therefore, in this study, a binary bat algorithm (BA)-based optimization method is used to identify the most important EEG channels for stroke rehabilitation. Thus, this method is suited to stroke survivors' needs by optimizing BCI technology and rehabilitation effectiveness. Raw EEG data are segmented and filtered using standard filters and the automated independent component analysis with wavelet transform (AICA-WT) denoising approach in an effort to address the issues mentioned above. Then, time-entropy-frequency (TEF) attributes are created by combining the effective features from the time, entropy, and frequency domains.