Abstract
The electro encephalo gram (EEG) signals classification playsa major role in developing assistive rehabilitation devices for physically disabled performs. In this context, EEG data were acquired from 20 healthy humans followed by the pre-processing and feature extraction process. After extracting the 12-time domain features, two well-known classifiers namely K-nearest neighbor (KNN) and multi-layer perceptron (MLP) were employed. The fivefold cross-validation approach was utilized for dividing data into training and testing purpose. The results indicated that the performance of MLP classifier was found better than the KNN classifier. MLP classifier achieved 95% classifier accuracy which is the best. The outcome of this study would be very useful for online development of EEG classification model as well as designing the EEG based wheelchair.
Highlights
The BCI system consists of four different units: (a) signal acquisition unit, (b) signal processing and classification unit which extracts the features of brain signals and converts those feature into device commands, (c) an output device and (d) an operating mechanism for guiding operation [1]
The results showed that the top five best features were Root Mean Square (RMS), Mean Absolute Value (MAV), LOG Detector (LD), Simple Square Integral (SSI) and VAR with the accuracy of 66.8±4.6%, 65.6±5.5 %, 64.9±5.1%, 58.5±3.6% and 57.7±3.4% with multi-layer perceptron (MLP) classifier respectively
This work reported the comparative analysis of 12-time domain features by employing the MLP and K-nearest neighbor (KNN) classifier in term of classification accuracy. 20 healthy human subjects were participated in three encephalo gram (EEG) data recording sessions in they imagine right and left-hand movements
Summary
The BCI system consists of four different units: (a) signal acquisition unit, (b) signal processing and classification unit which extracts the features of brain signals and converts those feature into device commands, (c) an output device and (d) an operating mechanism for guiding operation [1]. The implementation of such BCI system is based on four basic techniques (i) P300, (ii) slow cortical potentials, (iii) steady-state visually evoked potentials (SSVEP), and (iv) motor imagery (MI)[2]. The MI-based method has limited classification accuracy and results in poor reliability of the system[6][7]
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