# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 1 Introduction  
The World Health Organization (WHO) lists stroke as the second greatest cause of mortality globally because of the damage it causes to the brain through disruptions in cerebral circulation [1, 2]. One of the most debilitating forms of nervous system damage is hemiplegia, which describes partial or complete paralysis of one side of the body including the arm, leg, foot, and hand. Ischemic stroke, the most common type of stroke, causes interruption of cerebral perfusion, which results in rapid loss of brain function [3, 4]. Sixty percent or more of stroke survivors require rehabilitation due to permanent motor function impairment [5]. However, stroke survivors endure a wide range of disabilities, including visual and cognitive deficits, that have a cumulatively devastating effect on their ability tocarry out even the most basic of tasks. Accordingly, research into efficient treatment and rehabilitation of stroke victims has been a focus for many years [6]. An electroencephalogram (EEG) is a non-invasive method that estimates the electrical activity of the brain with good time resolution in order to define the results of a variety of mental tasks. By translating brain EEG signals into control instructions, brain-computer interfaces (BCIs) help people with physical impairments interact with the sensory world outside [7]. Motor imagery (MI) is the mental imagining of body movement without real muscle movement, and its accompanying rhythmic activities of the brain can be recorded and used as the input signals of BCI systems. Event-related desynchronization/synchronization (ERD/ERS) refers to the detection of the rhythmic power changes in the sensorimotor area within the Rolandic mu (9-13 Hz) and beta (13-30 Hz) frequency bands, which can be used to distinguish between various types of MI tasks [8]. Due to the fact that MI does not require any extraneous stimuli, BCIs based on MI have a wide variety of potential uses [9]. The volatility and complexity of EEG signals, however, cause difficulties for conventional MI-BCI decoding [7]. Crucial and difficult issues remain in enhancing EEG decoding capacity, extracting discriminative information from low signal-to-noise EEGs, and realizing reliable classification of various MI tasks [8]. Channel selection in EEG-based investigations is important to determine the subset of channels that is most informative in terms of capturing the proper neural signals for the chosen application [10]. Nevertheless, such considerations and their corresponding challenges may differ in other application contexts, and that can be seen when comparing stroke rehabilitation with emotion processing and dementia detection using an EEG-based dataset [11]. For example, stroke rehabilitation is characterized by the fact that with neurological defects resulting from the stroke event, there are certain modifications in the activity of the brain and the functioning of the neural networks. EEG data can also be characterized by specific features that distinguish them from healthy subjects or other patients, such as changes in the topological organization of networks, aberrant activity, and appearance of novel regulatory strategies [12]. Therefore, the approach to choosing the most significant EEG channels for stroke rehabilitation may need to be more selective and specific. The idea is to find out which of the channels provides the best recording of the neural characteristics of the particular motor, cognitive, or behavioral impairment which a stroke patient may present, and it will not be the same as the channels that would be ideal for other applications [13]. However, there are distinct approaches such as emotion processing and dementia detection that may employ different neurophysiological mechanisms and/or spectrotemporal patterns. For example, in emotion processing, the frequency band related to emotion processing can be spread across different cortical areas, and the aim of the channel selection could be to capture this distributed frequency activity.