Here the concept of Granger causality is employed to propose a new criterion for unsupervised learning that is appropriate in the case of temporally-dependent source signals. The basic idea is to identify two projections of a multivariate time series such that the Granger causality among the resulting pair of components is maximized.

Robust decoding and classification of brain patterns measured with electroencephalography (EEG) remains a major challenge for real-world (i.e. outside scientific lab and medical facilities) brain-computer interface (BCI) applications due to well documented inter- and intra-participant variability. Here, we present a large-scale benchmark evaluating over 340,000+ unique combinations of spatial and nonlinear EEG classification. Our methodological pipeline consists in combinations of Common Spatial Patterns (CSP), Riemannian geometry, functional connectivity, and fractal- or entropy-based features across three open-access EEG datasets. Unlike prior studies, our analysis operates at the per-participant level and across multiple frequency bands (8-15 Hz and 8-30 Hz), enabling direct assessment of both group-level performance and individual variability. Covariance tangent space projection (cov-tgsp) and CSP consistently achieved the highest average classification accuracies. However, their effectiveness was strongly dataset-dependent, and marked participant-level differences persisted, particularly in the most heterogeneous of the datasets. Importantly, nonlinear methods outperformed spatial approaches for specific individuals, underscoring the need for personalized pipeline selection. Our findings highlight that no universal 'one-size-fits-all' method can optimally decode EEG motor imagery patterns across all users or datasets. Future work will require adaptive, multimodal, and possibly novel approaches to fully address neurophysiological variability in practical BCI applications where the system can automatically adapt to what makes each user unique.

Reliable brain-computer interface (BCI) control of robots provides an intuitive and accessible means of human-robot interaction, particularly valuable for individuals with motor impairments. However, existing BCI-Robot systems face major limitations: electroencephalography (EEG) signals are noisy and unstable, target selection is often predefined and inflexible, and most studies remain restricted to simulation without closed-loop validation. These issues hinder real-world deployment in assistive scenarios. To address them, we propose a closed-loop BCI-AR-Robot system that integrates motor imagery (MI)-based EEG decoding, augmented reality (AR) neurofeedback, and robotic grasping for zero-touch operation. A 14-channel EEG headset enabled individualized MI calibration, a smartphone-based AR interface supported multi-target navigation with direction-congruent feedback to enhance stability, and the robotic arm combined decision outputs with vision-based pose estimation for autonomous grasping. Experiments are conducted to validate the framework: MI training achieved 93.1 percent accuracy with an average information transfer rate (ITR) of 14.8 bit/min; AR neurofeedback significantly improved sustained control (SCI = 0.210) and achieved the highest ITR (21.3 bit/min) compared with static, sham, and no-AR baselines; and closed-loop grasping achieved a 97.2 percent success rate with good efficiency and strong user-reported control. These results show that AR feedback substantially stabilizes EEG-based control and that the proposed framework enables robust zero-touch grasping, advancing assistive robotic applications and future modes of human-robot interaction.

Brain-computer interfaces (BCIs) based on motor imagery (MI) translate covert movement intentions into actionable commands, yet reliable decoding from non-invasive EEG remains challenging due to nonstationarity, low SNR, and subject variability. The RDWT performs an undecimated, multi-resolution subband decomposition that preserves temporal length and shift-invariance, enhancing sensorimotor rhythms while mitigating jitter and mild artifacts; subbands are fused via lightweight grouped 1-D convolutions and passed to a multi-kernel CNN for local temporal-spatial feature extraction, a grouped-query attention encoder for long-range context, and a compact TCN head for causal temporal integration. Our goal is to test whether this principled wavelet front end improves robustness precisely where BCIs typically fail--on the hardest subjects--and whether such gains persist on average across seeds under both intra-and inter-subject protocols. On BCI-IV-2a and BCI-IV-2b, across five seeds, RatioWaveNet improves worst-subject accuracy over the Transformer backbone by +0.17 / +0.42 percentage points (Sub-Dependent / LOSO) on 2a and by +1.07 / +2.54 percentage points on 2b, with consistent average-case gains These results indicate that a simple, trainable wavelet front end is an effective plug-in to strengthen Transformer-based BCIs, improving worst-case reliability without sacrificing efficiency. Introduction Brain-computer interfaces (BCIs) establish a direct communication pathway between neural activity and external devices by decoding brain signals into actionable commands, with the potential to transform both clinical rehabilitation and human-computer interactions and thereby improve quality of life [1, 2]. Among brain-sensing modalities, electroencephalography (EEG) is particularly well suited to real-world deployment because it is noninvasive, inexpensive, portable, and offers millisecond-level temporal resolution; these properties have underpinned a broad spectrum of EEG-based applications spanning cognitive skill assessment [3], driver vigilance estimation [4], emotion recognition [5], and human-robot interaction [6]. In healthcare, EEG is a key enabler of smart health ecosystems [7], supporting tasks such as automated sleep staging [8], seizure detection and monitoring [9], and neurorehabilitation after stroke [10]. Within this context, Motor Imagery (MI) - the covert rehearsal of movement without execution - has become one of the most widely adopted BCI paradigms [1].

This paper presents an Artificial Intelligence (AI) integrated novel approach to Brain - Computer Interface (BCI) - based wheelchair development, utilizing a motor imagery r ight - l eft - h and m ovement mechanism for control. The system is designed to simulate wheelchair navigation based on motor imagery right and left - hand movements using electroencephalogram (EEG) data. A pre - filtered dataset, obtained from an open - source EEG repository, was seg mented into arrays of 19x200 to capture the onset of hand movements. Th e data was acquired at a sampling frequency of 200Hz. The system integrates a Tkinter - based interface for simulating wheelchair movements, offering users a functional and intuitive control system. We propose a BiLSTM - BiGRU model that shows a superior test accuracy of 92. 26 % as compared with v arious machine learning baseline models, including XGBoost, EEGNet, and a transformer - based model . The Bi - LSTM - BiGRU attention - based model achieved a mean accuracy of 90.13 % through cross - validation, showcasing the potential of attention mechanisms in BCI applications. Keywords: Brain Computer Interface (BCI), BiLSTM - BiGRU, Raspberry Pi, E lectroencephalogram (EEG), Hybrid Deep learning 1. Introduction Brain - Computer Interfaces (BCIs) are advanced systems that establish direct communication between the human brain and external devices . In recent years, BCIs have been widely investigated for their potential to assist individuals with mobility impairments, offering novel pathways for restoring autonomy. This paper proposes a BCI - based wheelchair control system driven by electroencephalogra phy (EEG) signals associated with motor imagery. The proposed framework incorporates a variety of machine learning models with tailored hyperparameter optimization techniques, culminating in the deployment of a BiLSTM - BiGRU hybrid deep learning model for effective EEG signal classification.

Recent advances in electroencephalography (EEG) foundation models, which capture transferable EEG representations, have greatly accelerated the development of brain-computer interfaces (BCI). However, existing approaches still struggle to incorporate language instructions as prior constraints for EEG representation learning, limiting their ability to leverage the semantic knowledge inherent in language to unify different labels and tasks. To address this challenge, we present ELASTIQ, a foundation model for EEG-Language Alignment with Semantic Task Instruction and Querying. ELASTIQ integrates task-aware semantic guidance to produce structured and linguistically aligned EEG embeddings, thereby enhancing decoding robustness and transferability. In the pretraining stage, we introduce a joint Spectral-Temporal Reconstruction (STR) module, which combines frequency masking as a global spectral perturbation with two complementary temporal objectives: random masking to capture contextual dependencies and causal masking to model sequential dynamics. In the instruction tuning stage, we propose the Instruction-conditioned Q-Former (IQF), a query-based cross-attention transformer that injects instruction embeddings into EEG tokens and aligns them with textual label embeddings through learnable queries. We evaluate ELASTIQ on 20 datasets spanning motor imagery, emotion recognition, steady-state visual evoked potentials, covert speech, and healthcare tasks. ELASTIQ achieves state-of-the-art performance on 14 of the 20 datasets and obtains the best average results across all five task categories. Importantly, our analyses reveal for the first time that explicit task instructions serve as semantic priors guiding EEG embeddings into coherent and linguistically grounded spaces. The code and pre-trained weights will be released.

Individual differences in brain activity hinder the online application of electroencephalogram (EEG)-based brain computer interface (BCI) systems. To overcome this limitation, this study proposes an online adaptation algorithm for unseen subjects via dual-stage alignment and self-supervision. The alignment process begins by applying Euclidean alignment in the EEG data space and then updates batch normalization statistics in the representation space. Moreover, a self-supervised loss is designed to update the decoder. The loss is computed by soft pseudo-labels derived from the decoder as a proxy for the unknown ground truth, and is calibrated by Shannon entropy to facilitate self-supervised training. Experiments across five public datasets and seven decoders show the proposed algorithm can be integrated seamlessly regardless of BCI paradigm and decoder architecture. In each iteration, the decoder is updated with a single online trial, which yields average accuracy gains of 4.9% on steady-state visual evoked potentials (SSVEP) and 3.6% on motor imagery. These results support fast-calibration operation and show that the proposed algorithm has great potential for BCI applications.

Abstract--Current deep learning models for electroencephalog-raphy (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability. Although emerging foundation models aim for broader applicability, their rigid dependence on fixed, high-density multi-channel montages restricts their use across heterogeneous datasets and in missing-channel or practical low-channel settings. T o address these limitations, we introduce SingLEM, a self-supervised foundation model that learns robust, general-purpose representations from single-channel EEG, making it inherently hardware agnostic. The model employs a hybrid encoder architecture that combines convolutional layers to extract local features with a hierarchical transformer to model both short-and long-range temporal dependencies. When evaluated as a fixed feature extractor across six motor imagery and cognitive tasks, aggregated single-channel representations consistently outperformed leading multi-channel foundation models and handcrafted baselines. These results demonstrate that a single-channel approach can achieve state-of-the-art generalization while enabling fine-grained neurophysiological analysis and enhancing interpretability. LECTROENCEPHALOGRAPHY (EEG) is a noninvasive neurophysiological technique that measures brain activity through scalp electrodes. Because of to its high temporal resolution, portability, and affordability, EEG is widely applied in diverse domains, including brain-computer interfaces (BCIs) [1], sleep staging [2], seizure detection [3], [4], [5], clinical diagnosis [6], [7], and emotion recognition [8], [9], [10]. Despite its potential, EEG analysis is challenged by non-stationarity across subjects and sessions, susceptibility to noise (e.g., ocular or muscular artifacts), and low signal-to-noise ratios [11]. To address this, deep neural networks (DNNs) have emerged as the state-of-the-art paradigm, learning complex and task-relevant features automatically from raw data [12]. This work was supported in part by JSPS KAKENHI 23H00548. The work of Jamiyan Sukhbaatar was supported by the Mongolia-Japan Engineering for Education Development (MJEED) project.

-- Despite the growing success of deep learning (DL) in offline brain-computer interfaces (BCIs), its adoption in real-time applications remains limited due to three primary challenges. First, most DL solutions are designed for offline decoding, making the transition to online decoding unclear . Second, the use of sliding windows in online decoding substantially increases computational complexity. Third, DL models typically require large amounts of training data, which are often scarce in BCI applications. T o address these challenges and enable real-time, cross-subject decoding without subject-specific calibration, we introduce real-time adaptive pooling (RAP), a novel parameter-free method. RAP seamlessly modifies the pooling layers of existing offline DL models to meet online decoding requirements. It also reduces computational complexity during training by jointly decoding consecutive sliding windows. T o further alleviate data requirements, our method leverages source-free domain adaptation, enabling privacy-preserving adaptation across varying amounts of target data. Our results demonstrate that RAP provides a robust and efficient framework for real-time BCI applications. It preserves privacy, reduces calibration demands, and supports co-adaptive BCI systems, paving the way for broader adoption of DL in online BCIs. These findings lay a strong foundation for developing user-centered, high-performance BCIs that facilitate immediate feedback and user learning. I. INTRODUCTION A brain-computer interface (BCI) is a system that measures brain activity and converts it into functionally useful outputs, allowing it to replace, restore, enhance, supplement, or improve the brain's natural functions [1]. By facilitating direct communication between the brain and external devices, BCIs can assist individuals with disabilities, enhance cognitive and motor abilities, and improve human-computer interaction [2], [3].