Decrypting wrist movement from MEG signal using SVM classifier

Special issue on intelligent tools and techniques for signals, machines and automation

“…The classifier is employed in the identification of the elbow movement angle with an accuracy of 97.5%, for the identification of finger movements when typing keys with an accuracy of 98.7% and for hand movements with an accuracy of 97.6%. In [23], a SVM based classifier is used for translating magneto-encephalography (MEG) signals to the corresponding wrist movement and the results obtained have been compared to various other techniques employed for the same. In [24], neuro-fuzzy techniques have been employed in conjunction with failure modes and effects analysis (FMEA) to calculate the risk priority numbers (RPN) with the goal of improving the quality of service at hospitals.…”