On using AI for EEG-based BCI applications: problems, current challenges and future trends
Not on the map. Why?
Abstract (arXiv)
Imagine unlocking the power of the mind to communicate, create, and even interact with the world around us. Recent breakthroughs in Artificial Intelligence (AI), especially in how machines "see" and "understand" language, are now fueling exciting progress in decoding brain signals from scalp electroencephalography (EEG). Prima facie, this opens the door to revolutionary brain-computer interfaces (BCIs) designed for real life, moving beyond traditional uses to envision Brain-to-Speech, Brain-to-Image, and even a Brain-to-Internet of Things (BCIoT). However, the journey is not as straightforward as it was for Computer Vision (CV) and Natural Language Processing (NLP). Applying AI to real-world EEG-based BCIs, particularly in building powerful foundational models, presents unique and intricate hurdles that could affect their reliability. Here, we unfold a guided exploration of this dynamic and rapidly evolving research area. Rather than barely outlining a map of current endeavors and results, the goal is to provide a principled navigation of this hot and cutting-edge research landscape. We consider the basic paradigms that emerge from a causal perspective and the attendant challenges presented to AI-based models. Looking ahead, we then discuss promising research avenues that could overcome today's technological, methodological, and ethical limitations. Our aim is to lay out a clear roadmap for creating truly practical and effective EEG-based BCI solutions that can thrive in everyday environments.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 112 references that are in the map. First posted on arXiv 19 Jun 2025.
Builds on
22 of 112 references.
References · 22
- 2024Training Large Language Models to Reason in a Continuous Latent Spacecited by 797
- 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
- 2021Time-Series Representation Learning via Temporal and Contextual Contrastingcited by 345
- 2024Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCIcited by 327
- 2018Deep Learning in Spiking Neural Networkscited by 263
- 2021BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG datacited by 199
- 2023Causal Parrots: Large Language Models May Talk Causality But Are Not Causalcited by 150
- 2021Transformer-based Spatial-Temporal Feature Learning for EEG Decodingcited by 97
- 2018Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approachcited by 76
- 2023Neuro-GPT: Towards A Foundation Model for EEGcited by 74
- 2020Federated Transfer Learning for EEG Signal Classificationcited by 37
- 2024Causality for Large Language Modelscited by 25
10 more
- 2020An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computingcited by 16
- 2019Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classificationcited by 12
- 2023Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decodingcited by 9
- 2020Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain--Machine Interfacescited by 8
- 2024Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfacescited by 7
- 2023CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversioncited by 6
- 2021A New Neuromorphic Computing Approach for Epileptic Seizure Predictioncited by 5
- 2024User Identity Protection in EEG-based Brain-Computer Interfacescited by 5
- 2023Minding rights: Mapping ethical and legal foundations of 'neurorights'cited by 3
- 2024Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfacescited by 3
Built on by
Not cited yet.