Electroencephalogram based Brain-Computer Interface (EEG-BCI) employing motor Execution or imagery paradigms generally employs combination of multiple limbs namely right hand, left hand and foot to establish distinct control tasks. The generated neuronal patterns by multiple limb activity are distinct and have been offering promising impact on BCI-based rehabilitation and control applications for decades. However, a huge challenge for present BCI decoding systems lie in decoding finer actions of a single limb such as opening, closing, flexion and extension which is highly difficult for most present-day neural networks, on account of the highly overlapping brain-activations. The inherent high noise-to-signal ratio, inter-subject variability and intra-subject variability associated with the collected EEG signals makes the task challenging for even for deep learning networks which perform efficiently for multiple limb decoding. In this work, a novel network is introduced, named as Cycle-consistent Attention-based Spatio-Temporal Network (CASTNet), for the purposes of classifying open/close attempt based EEG signals of the right hand. The network uses spatio-temporal filters to extract distinguishable features associated with the motor attempts. The network further utilizes transformer layers to capture the long-term dependencies of EEG decoding network. Cycle-consistency is then applied towards the target data against the remaining training set in a subject-dependent setting. The network is validated upon a dataset comprising of 50 subjects performing open close hand movement attempts using right hand. The proposed network shows high efficacy in utilizing data in model adaptation scenarios.

Brain-computer interfaces (BCI) serve as an important tool in areas such as neurorehabilitation and constructing prostheses. Electroencephalogram (EEG) motor imagery (MI) signal is a common method used to communicate between the human brain and the computer interface. However, differentiating between multiple motor imagery signals may be challenging due to the presence of high noise-to-signal ratio and small dataset sizes. In this study, we propose a variational autoencoder and transformer-attention based convolutional neural network (SSTACNet) for multi-class EEG-based motor imagery classification. The SSTACNet model leverages upon variational autoencoders’ ability to measure the contrastive distance between two sets of inputs to perform data self-selection. The model further utilizes multi-head self-attention as well as spatial and temporal convolutional filters to achieve superior extraction of signal features. The model additionally utilizes the variational autoencoder’s ability to augment the dataset with feature-informed pseudo-data, achieving stronger classification results. The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV-2a dataset with an accuracy of 85.52% and 70.56% for the subject-dependent and subject-independent modes, respectively. Codes may be found at: https://github.com/NgHanWei/SSTACNet

To address the effect of both intra- and inter-subject variability in the EEG-based motor-imagery classification, adaptive schemes have been proposed whereby the pre-trained model. However, collection and labelling of additional target data is resource intensive. Furthermore, there may exist significant signal variations across time especially across multiple recording sessions which can result in model performance deterioration following adaptation. This introduces another challenging problem to training classifiers as they are typically unable to automatically determine which data is suitable for fine-tuning purposes, leading to some subjects facing performance drops even after adaptation. To address the data scarcity and variability in adaptive performance, we propose a novel machine-relearning Siamese architecture which utilizes few samples of unlabeled evaluation data to perform data efficient model re-learning. Machine re-learning optimally selects a sub-section of previously known data to update the model parameters. This is implemented via the use of comparative contrastive loss between the Gaussian distributions of target and known data to perform data selection. The highest subject-independent performance achieved an average (N=54) accuracy of 86.63% (±11.79%) without additional data and 88.23% (±10.36%) when utilizing a single unlabeled supplementary data for two-class motor imagery. The previous best accuracy on this dataset is 85.90% (±11.20%) using the best-known method in the literature. Therefore, significantly superior adaptation performance can be achieved while utilizing lesser amount of information from the target subject through reducing EEG feature variability in the training and fine-tuning sets. Codes may be found at: https://github.com/NgHanWei/EEG_Relearning