Data-driven Data Augmentation for Motor Imagery Brain-Computer Interface

Abstract Objective: Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing. Approach: We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a ‘baseline’ class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its outer layers with only 12-minute individual-related data. Main results: The model tested with the ‘EEG Motor Movement/Imagery Dataset’ outperforms the current state-of-the-art models by achieving a 99.38% accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of 99.46%. Significance: The proposed methods could foster future BCI applications relying on few-channel portable recording devices and individual-based training.

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Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

Brain Sciences ◽

10.3390/brainsci10100707 ◽

2020 ◽

Vol 10 (10) ◽

pp. 707

Author(s):

Luisa Velasquez-Martinez ◽

Julian Caicedo-Acosta ◽

Carlos Acosta-Medina ◽

Andres Alvarez-Meza ◽

German Castellanos-Dominguez

Keyword(s):

Motor Imagery ◽

Motor Skills ◽

Brain Computer Interface ◽

Data Driven ◽

Computer Interface ◽

Initial Training ◽

Subject Variability ◽

The Individual ◽

Indicator Evaluation ◽

The Brain

Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain–Computer Interface inefficiency of subjects.

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