Open research questions in EEG and Brain-Computer Interfaces

What the literature leaves open

• Second, to address cross-trial and cross-subject variability, future work will explore domain adaptation and transfer learning techniques to mitigate the impact of individual differences and trial-specific effects on decoding performance.

  Design and Decoding of a Novel Steering Motor Imagery Paradigm for Driving Brain–Computer Interfaces · 2026 · DOI

• Reliable seizure monitoring remains a persisting clinical challenge. Traditional electroencephalogram interpretation is susceptible to fatigue-related errors. Commercial software causes 3-8 false alarms per day, undermining clinical confidence.

  EEG anomaly detection using XGBoost and ConvLSTM model · 2026 · DOI

• Future work could explore augmented reality (AR) glasses as a stimulus carrier, utilizing their high-brightness displays and retinal projection technology to enhance outdoor adaptability. Despite the demonstrated feasibility of the system in hardware-in-the-loop simulations and real-world tests, several limitations remain to be explored in future research.

  UAV Target Detection and Tracking Integrating a Dynamic Brain–Computer Interface · 2026 · DOI

• Future research will focus on extending the proposed framework to multi-class EEG classification, integrating attention-based or transformer architectures within the ensemble, and exploring patient-specific adaptation strategies.

  A Voting-Based Ensemble Approach for Brain Disorder Detection Using Random Forest · 2026 · DOI

• Future work will focus on adaptive time–frequency partitions, physiologically informed baselines, and more efficient coalition sampling strategies. A promising direction consists of developing a variational extension of STF-KernelSHAP, in which the coalition distribution is learned from data rather than imposed through independent binary sampling.

  STF-KernelSHAP: A Model-Agnostic Space–Time–Frequency Shapley Framework for Physiologically Informed EEG Explainability · 2026 · DOI

• Future work should investigate extending the framework to multi-channel EEG montages (including F4 and O2 leads) and complementary polygraphic signals (EOG, EMG), comprehensive hyperparameter optimization, and validation on larger, multi-center datasets to strengthen these findings.

  Modulation-Based Feature Extraction for Robust Sleep Stage Classification Across Apnea-Based Cohorts · 2026 · DOI

• At this stage, our work targets patient-specific seizure detection, while cross-patient seizure detection and seizure prediction remain to be explored.

  Multiscale Cosine Convolution Neural Network for Robust and Interpretable Epileptic EEG Detection · 2026 · DOI

• The bench sessions were acquired under controlled static conditions, and the behaviour of the LDA edge artefact under artefact-corrupted inputs arising from physical movement, electrode displacement, or cable motion has not been evaluated.

  Hybrid Edge–Cloud Asymmetric Analytics for Portable Multimodal BCI Biosensors · 2026 · DOI

• One limitation of this study is that it was evaluated only on specific emotion and affective state classes under limited scenarios. Multiple emotion classes, heterogeneous user groups, and broader healthcare conditions have not yet been considered.

  EEG and ECG Wearable Biosensor-Based Affective State Analysis Using Deep Learning over 6G IoT Healthcare Networks · 2026 · DOI

• While research in the scalp-EEG domain is active with abundant data, the ear-EEG domain remains relatively underexplored, resulting in a lack of publicly available benchmark datasets.

  Deep Learning for Ear-EEG-Based Brain–Computer Interface: A Systematic Comparison and Design Insights · 2026 · DOI

• Further research is needed to improve the accuracy of the EEG measurements and the machine learning algorithm. The study could be replicated with a larger sample size and more diverse population. Other physiological signals, such as galvanic skin response and heart rate, could be explored as additional measures of MWL and MF.

  Towards a neuroadaptive augmented reality piano tutor: opportunities and challenges · 2026 · DOI

• The paper identifies a gap in the use of physiological signals for adaptive systems, which lack specificity and may not be suitable for rapid adaptive responses. The paper also identifies a gap in the development of neuro-adaptive systems that can detect changes in brain activity and adapt the difficulty of the tutorial.

  Towards a neuroadaptive augmented reality piano tutor: opportunities and challenges · 2026 · DOI

• Overall, these distribution-level comparisons suggest that the generated data do not perfectly match the full physiological distribution of real pre-ictal EEG; therefore, further validation is warranted, including expert review and methodological improvements to enhance low-frequency fidelity and overall distribution alignment.

  Pre-Ictal EEG Augmentation Based CDCGAN Model for Epileptic Seizure Prediction · 2026 · DOI

• Subsets with higher inter-channel correlation were not systematically more informative, suggesting that similarity alone is insufficient as a selection criterion.

  Assessing EEG Channel Similarity and Informational Relevance for Motor Tasks · 2026 · DOI

• The threshold sensitivity analysis and calibration baseline comparison introduced in this revision partially address this concern, but do not eliminate the underlying design choice to use a hand-specified rule; a fully data-driven, jointly-optimized calibration scheme that retains an explicit safety ceiling remains an open design problem.

  CLSP-REQA: a real-time quality-aware closed-loop seizure prediction framework with Mamba-BiLSTM and confidence-gated intervention · 2026 · DOI

• Future work should focus on the following: Developing novel Transformer and CNN–Transformer architectures that maintain very high performance and improve efficiency beyond media. This practice is still not well established.

  Efficient Feature Extraction for EEG-Based Classification: A Comparative Review of Deep Learning Models · 2026 · DOI

• Therefore, the generalisability of tfDM features across diseases and heterogeneous EEG datasets remains to be established. Several limitations of this study should be acknowledged. Additional cross-dataset validation showed above-chance classification using tfDM features in an independent cohort; however, this validation was limited to AD and a single independent dataset.

  Disease-specific intrinsic frequency alterations in electroencephalographic signals of Alzheimer’s disease, frontotemporal dementia, and Parkinson’s disease · 2026 · DOI

• The field is severely bounded by a persistent lack of standardized evaluation, a scarcity of uncorrupted human ground-truth records, the black-box opacity of deep networks, data leakage vulnerabilities during subject-dependent testing, and the severe diagnostic risk of generative hallucinations.

  Computational approaches to deep learning–driven EEG analysis for neurological disease diagnosis: a systematic review · 2026 · DOI

• More sophisticated machine learning and deep learning approaches could be investigated in future work to determine whether alternative feature representations and nonlinear models can better exploit the temporal and inter-subject variability present in the dataset.

  Neurophysiological Characterization of the BigP3BCI Dataset and Preliminary Machine Learning Evaluation for P300-Based Brain–Computer Interfaces · 2026 · DOI

• Electrical source imaging (ESI) estimates neural activity from scalp EEG, but its ability to reconstruct the temporal dynamics of spontaneous brain activity beyond source localization remains unclear.

  How well does electrical source imaging reconstruct neural time series? Evidence from simultaneous scalp and intracranial EEG recordings · 2026 · DOI

• EEG connectivity can distinguish individuals, but the persistence of its phase and magnitude structure across visits and functional states remains incompletely understood.

  Longitudinal persistence of EEG brain fingerprints:phase and complex coherency geometry across sessions andfunctional states · 2026 · DOI

• The scope is further limited to the EEG signal-processing and machine-learning pipeline; clinical factors such as vascular biomarkers, genetic markers, and cognitive measures are not systematically covered.

  From Acquisition to Validation: Methodological Dependencies and Reproducibility in EEG-Based Alzheimer’s Disease Detection · 2026 · DOI

• Over the next 3–5 years, four priorities should be advanced. First, construct large-scale pathological NVC databases to enhance decoding generalization.

  Applications of functional ultrasound brain-computer interfaces in neurological diseases: research progress based on neurovascular coupling · 2026 · DOI

• Paradigm: shapes observable signal content REST 5–20 min; ERPs Recording duration, eyes condition, and vigilance state inconsistently…

  From Acquisition to Validation: Methodological Dependencies and Reproducibility in EEG-Based Alzheimer’s Disease Detection · 2026 · DOI

• Further empirical investigation remains necessary to understand the effects of arts-based practices - Research on the self-reference effect could provide insights into the personal relevance of the imagery - Studies could collect participants' accounts of the imagery or ratings of engagement, presence…

  Changes in negative affect and resting-state EEG power following an EEG-derived generative reality art experience: a pilot pre–post study · 2026 · DOI

Most-cited papers in EEG and Brain-Computer Interfaces

• Removing electroencephalographic artifacts by blind source separation · Psychophysiology · 2000 · 2,875 citations
• NeuroKit2: A Python toolbox for neurophysiological signal processing · Behavior Research Methods · 2021 · 1,297 citations
• ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features · Psychophysiology · 2010 · 1,162 citations
• High-performance brain-to-text communication via handwriting · Nature · 2021 · 952 citations
• An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG · IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2021 · 685 citations
• A brain-computer interface that evokes tactile sensations improves robotic arm control · Science · 2021 · 625 citations
• Using Electroencephalography to Measure Cognitive Load · Educational Psychology Review · 2010 · 617 citations
• Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria · New England Journal of Medicine · 2021 · 570 citations
• Breaking the silence: Brain–computer interfaces (BCI) for communication and motor control · Psychophysiology · 2006 · 547 citations
• Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review · Neural Computing and Applications · 2021 · 543 citations

Most recent work

• EEG foundation models: a critical review of current progress and future directions · Journal of Neural Engineering · 2026
• Deep learning approaches for EEG-based healthcare applications: a comprehensive review · Frontiers in Human Neuroscience · 2026
• mm-HrtEMO: Non-Invasive Emotion Recognition via Heart Rate Using mm-Wave Sensing in Diverse Scenarios · IEEE Journal of Biomedical and Health Informatics · 2026
• Non-Invasive Brain-Computer Interfaces: Converging Frontiers in Neural Signal Decoding and Flexible Bioelectronics Integration · Nano-Micro Letters · 2026
• Hands-Free Mobility Control: A Low-Latency Asynchronous MI-BCI System for Real-Time Robotic Navigation With Cognitive-Ergonomic Enhancement · IEEE Transactions on Human-Machine Systems · 2026
• Standardizing EEG preprocessing for cross-site integration - the CLEAN pipeline · NeuroImage · 2026
• Reclaiming Hand Functions for Chronic Spinal Cord Injury with Epidural Brain-Computer Interface · medRxiv · 2026
• MSA-CNN: A lightweight multi-scale CNN with attention for sleep stage classification · Biomedical Signal Processing and Control · 2026
• Explainable Artificial Intelligence (XAI) for EEG Analysis: A Survey on Recent Trends and Advancements · AI · 2026
• Efficient Feature Extraction for EEG-Based Classification: A Comparative Review of Deep Learning Models · AI · 2026

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