Covert speech involves imagining speaking without audible sound or any movements.Decoding covert speech from electroencephalogram (EEG) is challenging due to a limited understanding of neural pronunciation mapping and the low signal-to-noise ratio of the signal. In this study, we developed a large-scale multi-utterance speech EEG dataset from 57 right-handed native English-speaking subjects, each performing covert and overt speech tasks by repeating the same word in five utterances within a ten-second duration. Given the spatio-temporal nature of the neural activation process during speech pronunciation, we developed a Functional Areas Spatio-temporal Transformer (FAST), an effective framework for converting EEG signals into tokens and utilizing transformer architecture for sequence encoding. Our results reveal distinct and interpretable speech neural features by the visualization of FAST-generated activation maps across frontal and temporal brain regions, with each word being covertly spoken, providing new insights into the discriminative features of the neural representation of covert speech. This is the first report of such a study, which provides interpretable evidence for speech decoding from EEG.

This article presents a rigorous investigation of the global dissipativity (GD) and quasi-synchronization (QS) of fractional-order complex-valued neural networks (FOCVNNs) with mixed delays and external disturbances. The considered mixed delays consist of both time-varying delays (TVDs) and infinite distributed delays (IDDs), which significantly complicate the dynamical analysis of fractional-order systems. To address this challenge, the fractional comparison principle is first extended to accommodate IDD. Based on this extension, a generalized fractional Halanay inequality is established, providing an effective analytical tool for fractional-order systems with mixed delays. By employing the proposed inequality, two sufficient conditions are derived to guarantee GD and QS of FOCVNNs with mixed delays and external disturbances under designed feedback control. Finally, the effectiveness and applicability of the theoretical analysis are demonstrated through two illustrative examples, including a standard numerical simulation and a secure communication scheme constructed from a fractional-order chaotic system with mixed delays.

Brain-computer interfaces (BCIs) hold great potential for motor recovery in post-stroke patients. However, the motor imagery decoding accuracy is limited by the non-stationarity of EEG signals across subjects and sessions. We propose CAT-Net: a Co-Adaptive Transfer learning network to simultaneously address the inter-subject variability and inter-session nonstationarity in EEG data. The proposed method selects a relevant subset of data from all the available subjects’ data to train an initial model, followed by subject-specific transfer learning from the initial model to the target subject to establish a pretrain model. Subsequently, online adaptive training is then applied to incrementally train the pretrain model using the data from previous sessions for the target subject. This proposed network using this unique co-adaptive training method is then evaluated on both upper and lower-limb neurorehabilitation EEG datasets comprising 358 sessions from 33 stroke patients. The results showed significant accuracy improvements, achieving averaged accuracies of 70.6% and 72.3% on the respective datasets, surpassing the state-of-the-art baselines.

Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous techniques emphasize learning spatial and short-term temporal patterns, there has been a limited emphasis on capturing the vital long-term contextual information associated with emotional cognitive processes. In order to address this discrepancy, we introduce a novel transformer model called emotion transformer (EmT). EmT is designed to excel in both generalized cross-subject electroencephalography (EEG) emotion classification and regression tasks. In EmT, EEG signals are transformed into a temporal graph format, creating a sequence of EEG feature graphs using a temporal graph construction (TGC) module. A novel residual multiview pyramid graph convolutional neural network (RMPG) module is then proposed to learn dynamic graph representations for each EEG feature graph within the series, and the learned representations of each graph are fused into one token. Furthermore, we design a temporal contextual transformer (TCT) module with two types of token mixers to learn the temporal contextual information. Finally, the task-specific output (TSO) module generates the desired outputs. Experiments on four publicly available datasets show that EmT achieves higher results than the baseline methods for both EEG emotion classification and regression tasks. The code is available at https://github.com/yi-ding-cs/EmT.

Due to the inter-subject variability of Electroencephalogram(EEG) signals, a long calibration time is required to collect a large number of labeled trials to calibrate classifier parameters before using the Brain-computer Interface(BCI). This challenge greatly limits the practical roll-out of BCIs. To address this problem, we propose a novel instance-based transfer learning framework named Riemannian Geometric Instance Filtering (RGIF) to reduce calibration time without sacrificing accuracy. A new inter-subject similarity metric based on Riemannian geometry is proposed to measure the similarity between a few trials from the target subject and adequate trials from source subjects. The classification model for the target subject is then trained with the help of abundant trials from similar source subjects with high similarity to the target subject. We evaluate our method on two open-source EEG datasets. The results show that our approach improves significantly compared with other baselines. Furthermore, compared with using all source subjects data, our method reduces the training time by at least half and achieves slightly better accuracy.

The brain–computer interface (BCI) technology improves the communication efficiency between people and Internet of Things (IoT) devices. BCI based on the steady-state visual evoked potential (SSVEP-BCI) is the preferred scheme for controlling devices because of its convenient operation, low training requirement, and high information transmission rate (ITR). Most signal acquisition devices for BCIs are used for medical diagnosis and scientific research and utilize multiple channels and wet electrodes to obtain high-quality signals. However, the practicability, wearability, and cost of the signal acquisition devices for real-life applications need to be considered, resulting in new requirements for the acquisition mode, the number of electrodes, power consumption, and signal processing methods. This article presents a wearable low-power collaborative sensing system based on a time mask window canonical correlation analysis method (TMW-CCA). An 8-array spring dry electrode signal acquisition device based on a flexible circuit board is designed to address the shortcomings of traditional wet electrode acquisition devices, such as high-power consumption, discomfort, and being unsuitable for long-time use. The proposed TMW-CCA method, which uses a dry electrode sensor to evaluate the time domain’s signal quality dynamically, exhibits 12.5% higher steady-state visual evoked potential recognition accuracy and 40% lower average power consumption (only 740 mW) than the benchmark.