Automated Classification of Hand-grip action on Objects using Machine Learning
This work addresses the need for brain-computer interface applications to assist disabled persons, but it appears incremental as it applies existing methods like DWT and neural networks to a specific classification task.
The paper tackled the problem of classifying correct and incorrect handgrip actions on objects using EEG data, achieving effective results tested on a dataset from 14 persons.
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.