Automated Classification of Hand-grip action on Objects using Machine Learning

TL;DR

This paper introduces an automated EEG-based classification system for handgrip actions on objects, aiming to enhance brain-computer interface applications for assisting disabled persons.

Contribution

It presents a novel approach combining EEG preprocessing, feature extraction with DWT and entropy, and neural network classification for handgrip response accuracy detection.

Findings

Effective classification of correct and incorrect handgrip responses.

Tested on EEG data from 14 individuals, showing promising results.

Potential for developing BCI devices for hand movement control.

Abstract

Brain computer interface is the current area of research to provide assistance to disabled persons. To cope up with the growing needs of BCI applications, this paper presents an automated classification scheme for handgrip actions on objects by using Electroencephalography (EEG) data. The presented approach focuses on investigation of classifying correct and incorrect handgrip responses for objects by using EEG recorded patterns. The method starts with preprocessing of data, followed by extraction of relevant features from the epoch data in the form of discrete wavelet transform (DWT), and entropy measures. After computing feature vectors, artificial neural network classifiers used to classify the patterns into correct and incorrect handgrips on different objects. The proposed method was tested on real dataset, which contains EEG recordings from 14 persons. The results showed that the proposed approach is effective and may be useful to develop a variety of BCI based devices to control hand movements.