EEG Signal Prediction for Motor Imagery Classification in Brain-Computer Interfaces

Abstract

Brain-computer interfaces (BCIs) based on motor imagery (MI) generally require EEG signals recorded from a large number of electrodes distributed across the cranial surface to achieve accurate MI classification. Not only does this entail long preparation times and high costs, but it also carries the risk of losing valuable information when an electrode is damaged, further limiting its practical applicability. In this study, a signal prediction-based method is proposed to achieve high accuracy in MI classification using EEG signals recorded from only a small number of electrodes. The signal prediction model was constructed using the elastic net regression technique, allowing for the estimation of EEG signals from 22 complete channels based on just 8 centrally located channels. The predicted EEG signals from the complete channels were used for feature extraction and MI classification. The results obtained indicate a notable efficacy of the proposed prediction method, showing an average performance of 78.16% in classification accuracy. The proposed method demonstrated superior performance compared to the traditional approach that used few-channel EEG and also achieved better results than the traditional method based on full-channel EEG. Although accuracy varies among subjects, from 62.30% to an impressive 95.24%, these data indicate the capability of the method to provide accurate estimates from a reduced set of electrodes. This performance highlights its potential to be implemented in practical MI-based BCI applications, thereby mitigating the time and cost constraints associated with systems that require a high density of electrodes.

Keywords: brain–computer interface (BCI); electroencephalography (EEG); motor imagery (MI); multiple regression analysis; regularization analysis; signal prediction.

Figure 1

A simplified illustration of SVM, where the filled shapes denote the support vectors. The margin refers to the distance between these support vectors [61].

Figure 2

Guideline of the proposed framework to improve the classification of these signals using a prediction and classification approach based on regression and spatial pattern analysis.

Figure 3

Timeline of the BCI Competition IV Dataset IIa database, of the motor imagery paradigm evaluated.

Figure 4

Electrode montage corresponding to the international 10–20 system. EEG channel configuration: numbering (left) and corresponding labels (right).

Figure 5

Position of the 8 strategically selected channels located in cortical areas highly related to MI. These positions correspond to scalp regions covering primary motor and premotor areas, such as the somatosensory cortex, which are essential for the neuronal representation of imagined movements (left). Extended configuration of 22 channels, predicted and extrapolated from the initial 8 channels (right).

Figure 6

Scheme for preprocessing the EEG input signal for MI-EEG classification.

Figure 7

Comparison between the recorded EEG and the estimated EEG of subject 9.

Figure 8

Comparison between the recorded EEG and the estimated EEG of subject 2.

Figure 9

EEG topoplot of the CSP components of the subjects. These maps represent the spatial distribution of neuronal activity derived from MI analysis, using a regularized model where the 6 CSP components are calculated for each subject. The patterns reveal variations in cortical activation among subjects, highlighting lateralization and individual differences in the motor areas involved during MI tasks.

Figure 10

Average confusion matrices across all subjects.