Human brain research is essential to neuroscience, and the integration of electroencephalography (EEG) with artificial intelligence (AI) has emerged as a vital area of exploration. This synergy promises to advance our understanding of neural mechanisms and improve clinical outcomes.
The AI into EEG signal analysis has marked a significant evolution in neuroscience, progressing from basic machine learning classifiers to advanced deep learning models. This development has enhanced the accuracy and efficiency of EEG interpretation. As early as 1996, Kalcher et al. introduced the Graz Brain-Computer Interface (BCI) II[
1], employing machine learning for online classification of EEG patterns associated with motor imagery to facilitate human-computer communication. This represented one of the initial practical applications of AI in EEG processing, establishing foundational methods for real-time signal decoding. Another key advancement was in 2004, with Lemm et al.’s probabilistic model for classifying imaginary hand movements from EEG signals using wavelet analysis and machine learning techniques[
2]. This work improved classification accuracy in motor imagery tasks, advancing BCI capabilities. The advent of deep learning technologies has enabled AI models to analyze EEG signals without extensive feature engineering. For instance, in 2016, Tabar and Halici applied a novel deep learning approach to classify EEG motor imagery signals using only a small number of samples[
3].
With the rapid progress of modern neurology and neurosurgery, several novel techniques have been invented to obtain detailed information from the human brain. In EEG-AI integration, current approaches primarily include convolutional neural networks (CNNs) for signal classification, recurrent neural networks (RNNs) for temporal analysis, and transformer-based architectures for handling noisy data. These methods excel in feature extraction without manual intervention, improving efficiency in real-time applications. Key directions encompass clinical diagnostics, where AI aids in epilepsy seizure detection and mental health biomarker identification; neurotechnology, focusing on BCIs for assistive devices; and cognitive science, decoding emotions and attention for human-AI collaboration. Recent studies highlight AI-enhanced portable EEG systems for point-of-care use, addressing challenges like data variability and privacy.
Several national projects have been launched to advance brain research, with a focus on AI integration. In the United States, the BRAIN Initiative[
4], announced in 2013, focuses on innovative neurotechnologies, including AI for EEG analysis in dynamic brain imaging and disease detection. National Institutes of Health (NIH) funding supports AI-enhanced EEG for BCI and cognitive studies, with ethical oversight through general AI guidelines. The “China Brain Project” (The Science & Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Technology Major Project)[
5], launched in 2021, also emphasizes AI-brain fusion, including brain-computer interfaces that leverage EEG for human-machine interaction, aiming to conquer diseases such as Alzheimer’s and cerebrovascular disorders while developing brain-inspired computing. Related supportive guidelines, such as the Ethical Guidelines for Medical Research Involving Human Neurotechnologies[
6], provide operational guidance for ethical issues that may arise in BCI development. Furthermore, China’s National Medical Products Administration has approved several EEG-AI tools for epilepsy and dementia diagnostics. Compared with research on animal models, funding for direct studies of the human brain remains relatively insufficient. Collaboration among policymakers, neuroscientists, and clinicians is crucial to address these challenges.
Human Brain promotes research manuscripts and reviews on EEG-AI integration, from signal denoising algorithms to neurofeedback applications. We encourage readers to engage in this dialogue to shape the future of brain science.
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