Hand Movement Imagery Task Classification using Fractal Dimension Feature

In this paper, a nonstimulus-based Brain Machine Interface (BMI) approach is used to acquire the brain signal from ten different subjects using 19 channel EEG electrodes while performing four different hand movement imaginary tasks. Three different Fractal Dimension algorithm namely Box counting algorithm, Higuchi algorithm, and Detrended fluctuation algorithm are used to extract fractal dimension features from recorded EEG signal and associated with the respective mental tasks. Three Feed-Forward Neural Network model is developed. The performance of the three Neural Network model is evaluated in term of classification rate and compared. The performance of the developed network models are evaluated through simulation. It is observed that the neural network model trained with Higuchi algorithm has contributed high classification accuracy with the better training and testing time for all 10 subjects. The result clearly indicates that the Higuchi fractal dimension algorithm can be used as a feature to classify motor imagery task for the proposed BMI system

Towards correlation-based time window selection method for motor imagery BCIs

The start of the cue is often used to initiate the feature window used to control motor imagery (MI)-based brain-computer interface (BCI) systems. However, the time latency during an MI period varies between trials for each participant. Fixing the starting time point of MI features can lead to decreased system performance in MI-based BCI systems. To address this issue, we propose a novel correlation-based time window selection (CTWS) algorithm for MI-based BCIs. Specifically, the optimized reference signals for each class were selected based on correlation analysis and performance evaluation. Furthermore, the starting points of time windows for both training and testing samples were adjusted using correlation analysis. Finally, the feature extraction and classification algorithms were used to calculate the classification accuracy. With two datasets, the results demonstrate that the CTWS algorithm significantly improved the system performance when compared to directly using feature extraction approaches. Importantly, the average improvement in accuracy of the CTWS algorithm on the datasets of healthy participants and stroke patients was 16.72% and 5.24%, respectively when compared to traditional common spatial pattern (CSP) algorithm. In addition, the average accuracy increased 7.36% and 9.29%, respectively when the CTWS was used in conjunction with Sub-Alpha-Beta Log-Det Divergences (Sub-ABLD) algorithm. These findings suggest that the proposed CTWS algorithm holds promise as a general feature extraction approach for MI-based BCIs