By: Aayush Diwate, Anushree Ninawe, Ayushman Namdeo, and M. V. Jadhav Jadhav
1-Student, Department of Electronics & Communication Engineering, Singhad College of Engineering, Pune, Maharashtra, India.
2-Student, Department of Electronics & Communication Engineering, Singhad College of Engineering, Pune, Maharashtra, India.
3-Student, Department of Electronics & Communication Engineering, Singhad College of Engineering, Pune, Maharashtra, India.
4-Professor, Department of Electronics & Communication Engineering, Singhad College of Engineering, Pune, Maharashtra, India.
This study delves into the intricacies of analyzing EEG signals associated with arm movement motor imagery. We explore established techniques across three key stages: data pre-processing, feature extraction, and classification for movement identification. By critically evaluating the strengths and weaknesses of each approach, we optimize the analysis pipeline for robust decoding of arm movement intent. This work empowers researchers and practitioners to transform raw EEG data into actionable insights, opening doors to diverse applications in medical rehabilitation, industrial control, and immersive entertainment. Notably, it represents a significant step towards harnessing the potential of EEG-driven Brain-Computer Interfaces (BCIs) for developing transformative assistive technologies.
Keywords: Brain Computer Interface (BCI), electroencephalography (EEG), Data Pre-processing, Feature Extraction, Classification
Citation:
Refrences:
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