Task‐Specific <scp>Electroencephlogram</scp> Analysis: A Novel <scp>ICA</scp> and Dynamic Multi‐Stage Clustering Approach for <scp>Neural</scp> Signal Processing

ABSTRACT Electroencephalogram (EEG) plays a vital role in identifying the neural activities and human behaviors. EEG is a high‐temporal‐resolution signal that is contaminated and influenced by various external factors and artifacts. It is very difficult to identify and analyze the EEG signal when it is contaminated by artifacts. The impact of artifacts in EEG signals became an open challenge in EEG signal processing. Even though many techniques and methods are available to remove the artifacts in EEG, analyzing the contaminated EEG signals remains a significant challenge. The signal is polluted by different artifacts like eye blink, muscle activities, environmental interfaces, and so on. Compared with other artifacts and noises, task‐specific artifacts such as motor movement and motor imagery have a greater impact on EEG signals due to their direct inference on cognitive signals, and isolating task‐specific artifacts from the EEG signal is difficult. To overcome these challenges, a novel hybrid task‐specific Dynamic Multistage k ‐Means Clustering algorithm (TS‐DMKC) has been proposed that detects and extracts the processed EEG signal, which will be helpful for all the EEG applications. Initially, the motor movement and motor imagery artifacts are distinguished using the Fast ICA by applying the filtering threshold. Then, a multistage k ‐means clustering algorithm is employed to isolate the artifacts in different clusters dynamically, and the cluster effectiveness is calculated by using the silhouette score. This proposed novel hybrid algorithm will be useful in various EEG applications like neuroscience, medical diagnosis, brain–computer interface, authentication, brain signal analysis, and gaming. The experimental results demonstrate that the proposed algorithm TS‐DMKC achieved a classification accuracy of 94.2% using the Physionet motor movement/imagery dataset, reducing task‐dependent variability and offering more robust and reliable EEG systems.