Brain signal decoding is essential for unraveling the intricate neural processes in the human brain. Various monitoring modalities, including electroencephalography (EEG), mag-netoencephalography (MEG), and electrocorticography (ECoG), provide valuable insights into cortical activities. However, these modalities exhibit variations in waveform, frequency resolution, and signal properties, posing challenges for unified decoding methods. This paper introduces a compact end-to-end multi-branch convolutional neural network (CNN) architecture designed to decode brain signals from diverse modalities. The model integrates designated feature extractors, ensuring versatile performance across modalities and paradigms. The architecture addresses data variability, expands receptive fields, and enhances generalizability, surpassing existing brain signal decoders on two EEG datasets (achieving accuracies of 77.89% and 72.25%), one MEG dataset (52.97%), and one ECoG dataset (93.31%). The proposed model exhibits robustness without requiring manual tailoring and signifies a substantial advancement in the general decoding of brain signals.

Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL)-based EEG decoders offer improved performances, the development of geometric learning (GL) has attracted much attention for offering exceptional robustness in decoding noisy EEG data. However, there is a lack of studies on the merged use of deep neural networks (DNNs) and geometric learning for EEG decoding. We herein propose a manifold attention network (mAtt), a novel geometric deep learning (GDL)-based model, featuring a manifold attention mechanism that characterizes spatiotemporal representations of EEG data fully on a Riemannian symmetric positive definite (SPD). The evaluation of the proposed mAtt on both time-synchronous and -asyncronous EEG datasets suggests its superiority over other leading DL methods for general EEG decoding. Furthermore, analysis of model interpretation reveals the capability of mAtt in capturing informative EEG features and handling the non-stationarity of brain dynamics.

Recently, the advances in passive brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have shed light on real-world neuromonitoring technologies. However, human variability in the EEG activities hinders the development of practical applications of EEG-based BCI. To tackle this problem, many transfer-learning techniques perform supervised calibration. This kind of calibration approach requires task-relevant data, which is impractical in real-life scenarios such as drowsiness during driving. This study presents a transfer-learning framework for EEG decoding based on the low-dimensional representations of subjects learned from the pre-trial EEG. Tensor decomposition was applied to the pre-trial EEG of subjects to extract the underlying characteristics in subject, spatial, and spectral domains. Then, the proposed framework assessed the characteristics to obtain the low-dimensional subject representations such that the subjects with similar brain dynamics can be identified. This method can leverage the existing data from other users, and a small number of data from a rapid, non-task, unsupervised calibration from a new user to build an accurate BCI. Our results demonstrated that, in terms of prediction accuracy, the proposed low-dimensional subject representation-based transfer learning (LDSR-TL) framework outperformed the random selection, and the Riemannian manifold approach in cognitive-state tracking, while requiring fewer training data. The results can greatly improve the practicability, and usability of EEG-based BCI in the real world.

High-speed steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been developed to enable the communications between the human brain and external environments. One of the major issues in the real-world applications of SSVEP-BCIs is the laborious and time-consuming calibration process, triggering the development of transfer-learning approaches to leverage existing data from other users. A comprehensive investigation on the inter-and intra-subject variability in SSVEP data is thus needed to provide insight for designing future transfer-learning frameworks for SSVEP-BCIs. We hereby present the first study that systematically and quantitatively assesses the variability in SSVEP data, where the sources of inter-and intra-subject variability at low-and high-frequency range were identified using Fisher's discriminant ratios (FDRs). The insights gained from this work could drive the future developments of transfer-learning approaches to minimize the calibration efforts in high-speed SSVEP BCIs.

This study proposes a flexible method to track and quantify the ac-coupled gain variations in electrode to tissue interface (ETI) of adhesive/reusable electrodes. Particularly, this study focuses on the effects of multiplicative motion artifacts (MMA) on ETIs and proposes a continuously monitoring technique to assess ETI multiplicative variations. The proposed method only requires one additional channel of self electrocardiogram (self-ECG). Both gel-covered and foam-based adhesive electrodes were tested. Empirical results show that the ETI multiplicative variations can be characterized by the proposed convenient method. Meanwhile, we successfully demonstrate different characteristics for two types of electrodes under five motion postures including suppress motion, fist/stretch palm, twist, bent inward and arm lift, Furthermore, The long-term degradation experiment illustrates that the average characteristics variation of ETI is less than 6% within 3-day of use.

On the pathway from laboratory settings to real world environment, a major challenge on the development of a robust electroencephalogram (EEG)-based brain-computer interface (BCI) is to collect a significant amount of informative training data from each individual, which is labor intensive and time-consuming and thereby significantly hinders the applications of BCIs in real-world settings. A possible remedy for this problem is to leverage existing data from other subjects. However, substantial inter-subject variability of human EEG data could deteriorate more than improve the BCI performance. This study proposes a new transfer learning (TL)-based method that exploits a subject's pilot data to select auxiliary data from other subjects to enhance the performance of an EEG-based BCI for drowsiness detection. This method is based on our previous findings that the EEG correlates of drowsiness were stable within individuals across sessions and an individual's pilot data could be used as calibration/training data to build a robust drowsiness detector. Empirical results of this study suggested that the feasibility of leveraging existing BCI models built by other subjects' data and a relatively small amount of subject-specific pilot data to develop a BCI that can outperform the BCI based solely on the pilot data of the subject.

Motion sickness is a common symptom that occurs when the brain receives conflicting information about the sensation of movement. Many motion sickness biomarkers have been identified, and electroencephalogram (EEG)-based motion sickness level estimation was found feasible in our previous study. This study employs genetic feature selection to find a subset of EEG features that can further improve estimation performance over the correlation-based method reported in the previous studies. The features selected by genetic feature selection were very different from those obtained by correlation analysis. Results of this study demonstrate that genetic feature selection is a very effective method to optimize the estimation of motion-sickness level. This demonstration could lead to a practical system for noninvasive monitoring of the motion sickness of individuals in real-world environments.

Motion sickness is a normal response to real, perceived, or even anticipated movement. People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Motion sickness occurs when the body, the inner ear, and the eyes send conflicting signals to the brain. Sensory conflict theory that came about in the 1970's has become the most widely accepted theorem of motion-sickness among scientists [1]. The theory proposed that the conflict between the incoming sensory inputs could induce motion- sickness. However, some new research studies have appeared to tackle the issue of the vestibular function in central nervous system (CNS). In the previous human subject studies, researchers attempt to confirm the brain areas involved in the conflict in multi-modal sensory systems by means of clinical or anatomical methods. Our past studies had investigated the EEG activities correlated with motion sickness in a virtual-reality based driving simulator. We found that the parietal, motor, occipital brain regions exhibited significant EEG power changes in response to vestibular and visual stimuli. Based on these experimental results, we attempt to implement an EEG-based evaluation system to estimate subject's motion sickness level upon the major EEG power spectra from these motion sickness related brain area in this study. The evaluation system can be applied to early detect the subject's motion sickness level and prevent the uncomfortable syndromes occurred in advance in our daily life.