Electroencephalography (EEG) is a non-invasive technique used to record the electrical activity of the brain. It captures electrical signals representing brain activity by placing electrodes on the scalp. Due to its high temporal resolution and low cost, EEG is widely used to analyse brain activity patterns. However, due to the low signal-to-noise ratio and large individualized variations of EEG signal, traditional signal analysis methods have limited the practical use of EEG. In recent years, Artificial Intelligence (AI) has become an attractive research field and revolutionized the analysis of EEG signals. AI's powerful data processing capabilities enable high-precision interpretation of complex EEG signals, allowing researchers to further understand brain signals and reveal patterns of brain activity associated with different cognitive tasks. As a result, EEG is more widely used in clinical diagnosis, brain-computer interfaces (BCI), brain function research, and other fields.
To further advance the practical references of using AI for EEG signal analysis, we need to address the issues of robustness and interpretability. Robustness relates to the performance of AI models in different application scenarios. AI models need to reduce the effect of noise in EEG signals and remove task irrelevant signal features during the feature extraction procedure. At the same time, AI model needs to overcome the individual differences in EEG signals. For example, applying methods like migration learning, adversarial learning to improve the model's generalization performance in cross-subject scenarios. Second, in order to widely adopt AI models in clinical research, the decision-making process of AI models needs to be interpretable. This includes the use of, for example, Spiking Neural Networks (SNN) to model the generation and propagation of electrical signals in the brain. Alternatively, using techniques including feature visualization, heat map analysis, etc. to analyse the model's prediction process and results to ensure that they are consistent with existing physiological knowledge.
Processing and analysis of EEG signals by AI can identify and explain network physiology, which contain the interaction of brain activity with other physiological systems of human. This combination could improve the understanding of the complex relationships between the brain and overall physiological systems and contribute to the development of more precise diagnostic and therapeutic approaches to brain disorders.
The scope of the Research Topic encompasses cutting-edge research and developments at the intersection of artificial intelligence and EEG signal analysis. We invite contributors to explore various themes, including but not limited to:
1. Advanced AI algorithms for EEG signal processing.
2. EEG-based disease diagnosis and monitoring using artificial intelligence.
3. Applications of EEG and AI in brain-computer interfaces (BCI).
4. Real-time EEG analysis with AI for clinical applications.
5. Applications of generative AI to generate text, speech, and images from EEG signals.
6. Interpretability of AI models applied to EEG data.
7. AI based EEG analysis methods in cross-subject and cross session scenarios.
8. Novel data preprocessing and noise reduction methods for EEG data.
Keywords:
Artificial Intelligence, EEG, Network Physiology, Signal Processing, Multivariate Time Series, Deep Learning
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Electroencephalography (EEG) is a non-invasive technique used to record the electrical activity of the brain. It captures electrical signals representing brain activity by placing electrodes on the scalp. Due to its high temporal resolution and low cost, EEG is widely used to analyse brain activity patterns. However, due to the low signal-to-noise ratio and large individualized variations of EEG signal, traditional signal analysis methods have limited the practical use of EEG. In recent years, Artificial Intelligence (AI) has become an attractive research field and revolutionized the analysis of EEG signals. AI's powerful data processing capabilities enable high-precision interpretation of complex EEG signals, allowing researchers to further understand brain signals and reveal patterns of brain activity associated with different cognitive tasks. As a result, EEG is more widely used in clinical diagnosis, brain-computer interfaces (BCI), brain function research, and other fields.
To further advance the practical references of using AI for EEG signal analysis, we need to address the issues of robustness and interpretability. Robustness relates to the performance of AI models in different application scenarios. AI models need to reduce the effect of noise in EEG signals and remove task irrelevant signal features during the feature extraction procedure. At the same time, AI model needs to overcome the individual differences in EEG signals. For example, applying methods like migration learning, adversarial learning to improve the model's generalization performance in cross-subject scenarios. Second, in order to widely adopt AI models in clinical research, the decision-making process of AI models needs to be interpretable. This includes the use of, for example, Spiking Neural Networks (SNN) to model the generation and propagation of electrical signals in the brain. Alternatively, using techniques including feature visualization, heat map analysis, etc. to analyse the model's prediction process and results to ensure that they are consistent with existing physiological knowledge.
Processing and analysis of EEG signals by AI can identify and explain network physiology, which contain the interaction of brain activity with other physiological systems of human. This combination could improve the understanding of the complex relationships between the brain and overall physiological systems and contribute to the development of more precise diagnostic and therapeutic approaches to brain disorders.
The scope of the Research Topic encompasses cutting-edge research and developments at the intersection of artificial intelligence and EEG signal analysis. We invite contributors to explore various themes, including but not limited to:
1. Advanced AI algorithms for EEG signal processing.
2. EEG-based disease diagnosis and monitoring using artificial intelligence.
3. Applications of EEG and AI in brain-computer interfaces (BCI).
4. Real-time EEG analysis with AI for clinical applications.
5. Applications of generative AI to generate text, speech, and images from EEG signals.
6. Interpretability of AI models applied to EEG data.
7. AI based EEG analysis methods in cross-subject and cross session scenarios.
8. Novel data preprocessing and noise reduction methods for EEG data.
Keywords:
Artificial Intelligence, EEG, Network Physiology, Signal Processing, Multivariate Time Series, Deep Learning
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.