Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models

Abstract

Brain-computer interfaces (BCIs) establish a communication pathway between the human brain and external devices by decoding neural signals. This study focuses on enhancing the classification of Motor Imagery (MI) within BCI systems by leveraging advanced machine learning and deep learning techniques. The accurate classification of electroencephalogram (EEG) data is crucial for enhancing BCI performance. The BCI architecture processes electroencephalography signals through three critical stages: data pre-processing, feature extraction, and classification. The research evaluates the performance of five traditional machine learning classifiers- K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB)-using the "PhysioNet EEG Motor Movement/Imagery Dataset". This dataset encompasses EEG data from various motor tasks, including both actual and imagined movements. Among the traditional classifiers, Random Forest achieved the highest accuracy of 91%, underscoring its efficacy in motor imagery classification within BCI systems. In addition to conventional approaches, the study also explores deep learning techniques, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks yielding accuracies of 88.18% and 16.13%, respectively. However, the proposed hybrid model, which synergistically combines CNN and LSTM, significantly surpasses both traditional machine learning and individual deep learning methods, achieving an exceptional accuracy of 96.06%. This substantial improvement highlights the potential of hybrid deep learning models to advance the state of the art in BCI systems, offering a more robust and precise approach to motor imagery classification.

Keywords: BCI; Classification; Deep learning; Disabilities; EEG; GAN’s; Machine learning; Motor imagery; Riemannian geometry.

Fig. 1

General BCI architecture.

Fig. 2

EEG signal difference before and after data pre-processing.

Fig. 3

Workflow of proposed methodology.

Fig. 4

Illustrates six regions of interest (ROIs) located in close proximity to the sensorimotor cortex. These ROIs, designated as 1 to 6, provide a visual representation of the 64 EEG electrode positions on the scalp. The electrode locations are marked by small circles and labelled according to standard nomenclature. The channels forming each ROI are highlighted in the table (see Table 3 for details).

Fig. 5

Visualization of frequency and time domain features.

Fig. 6

Architecture of proposed hybrid model (CNN + LSTM).

Fig. 7

ROC curves for different classifier in ROI.

Fig. 8

(a) Visualize the raw EEG data of left- and right-hand vs. (b) visualize the left- and right-hand movement using proposed model.

Fig. 9

Using Riemannian distance classifying the left-hand verses right hand movement.

Fig. 10

Confusion metric of (a) Decision Tree, (b) RF, (c) LR, (d) SVM, (e) KNN, and (f) CNN.

Fig. 11

Confusion metric of (g) LSTM, and (h) proposed hybrid LSTM + CNN.

Fig. 12

Classification task: loss and accuracy graph for proposed model.

Fig. 13

Comparative analysis of various state-of-the-art models based on their accuracy percentages. Source: Prepared by the authors based on^,.