A Novel CNN With Sliding Window Technique for Enhanced Classification of MI-EEG Sensor Data

• Published in: IEEE sensors journal Vol. 25; no. 3; pp. 4777 - 4786
• Main Authors: Singh, Kamal, Singha, Nitin, Jaswal, Gaurav, Bhalaik, Swati
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Accuracy; Artificial neural networks; Brain modeling; Brain-computer interface (BCI); Classification; convolutional neural network (CNN); Convolutional neural networks; Data models; Datasets; deep learning (DL); Electrodes; Electroencephalography; electroencephalography (EEG); Feature extraction; Filtering; Hand (anatomy); Human-computer interface; Machine learning; machine learning (ML); motor imagery (MI); Motors; Parameter sensitivity; Performance evaluation; sensor; Sensors; Signal processing; Sliding

The major challenge in fully using the motor imagery (MI)-based brain-computer interface (MI-BCI) capabilities is accurately classifying the MI electroencephalography (MI-EEG) signals. Despite numerous advancements in signal processing and deep learning (DL) techniques, there is significant scope fo...