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Enhancing EEG Analysis with AI: Developing a Tailored Foundational Model for EEG Signal Classification
This project aims to revolutionize the analysis of electroencephalography (EEG) data by developing a specialized foundational model utilizing the principles of artificial intelligence. Despite the critical role of EEG in diagnosing and treating neurological disorders, challenges such as low signal-to-noise ratios and complex signal patterns hinder practical analysis. By adapting strategies from successful domains like natural language processing and computer vision, this project will build a machine learning model tailored for EEG signals. The model will undergo extensive pre-training on diverse EEG datasets to establish a robust understanding of neural activities, followed by fine-tuning for specific clinical tasks such as seizure detection and sleep stage classification. Our approach promises to enhance the accuracy, efficiency, and accessibility of EEG diagnostics, paving the way for improved patient outcomes. Validation and testing using standard performance metrics will measure the model's efficacy, setting a new standard in EEG analysis.
Keywords: EEG Analysis, Foundational Models, Large Language Models, Machine Learning, Deep Learning, Transfer Learning, Signal Processing
Electroencephalography (EEG) is a fundamental tool in neuroscience, allowing us to monitor the brain's electrical activity non-invasively. It is beneficial for diagnosing and treating neurological disorders, but interpreting EEG data can be tricky due to many factors, such as low signal-to-noise ratio and the inherent complexity of EEG signals.
Large Language Models (LLMs), such as OpenAI’s GPT series, are a specific type of Foundational Model designed to understand, generate, and manipulate human language. LLMs are trained on extensive collections of text data, allowing them to learn a wide range of language patterns and nuances. This training enables them to perform various language-related tasks, from simple text generation to more complex applications like summarization, translation, and answering questions across multiple domains.
Foundational Models, a broader category, include LLMs and models designed for other data types, such as images, audio, and time-series. The common thread among all Foundational Models is their training approach: they are typically pre-trained on a large, diverse set of data to develop a broad understanding of a particular type of input, whether text, visual content, or EEG signals. After this extensive pre-training phase, these models are fine-tuned on more specific datasets or tasks to adapt their capabilities to more specialized applications.
Foundation models, known for their extensive pre-training on large datasets before being fine-tuned for specific tasks, have dramatically altered the landscape in natural language processing and computer vision. Nevertheless, their application in interpreting the complexities inherent in EEG data is still developing. This project proposes to develop a sophisticated foundational model specifically for EEG analysis, harnessing the capabilities of deep learning and AI.
Our project aims to make EEG analysis more accessible and accurate using the power of artificial intelligence. We plan to build a specialized foundational model for EEG data in this project.
Here is what we are going to do:
1. **Dataset Compilation**: We will gather various open-source EEG datasets. We will focus on ensuring the data is diverse, covering different demographics, conditions, and ways the data was collected.
2. **Model Architecture Design**: Next, we will develop a neural network architecture tailored for EEG data. We’ll take inspiration from successful models in other areas and adapt them to meet our needs.
3. **Pretraining**: We will train our model using the datasets we've compiled. We'll use transfer and semi-supervised learning to make the most of our data.
4. **Fine-Tuning**: We will fine-tune our model using a smaller, more specific set of data for tasks like detecting seizures or classifying sleep stages.
5. **Validation and Testing**: Finally, we will test our model to see how well it performs. We'll use metrics like accuracy, precision, recall, and the F1-score to evaluate and compare it to existing methods.
**Requirements**
Strong programming skills in Python
Machine Learning background (courses etc.)
**Related literature**
Jiang, Wei-Bang, Li-Ming Zhao, and Bao-Liang "Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI" ICLR 2024
Wang, Christopher, et al. "BrainBERT: Self-supervised representation learning for intracranial recordings." ICLR 2023.
Cui, Wenhui, et al. "Neuro-gpt: Developing a foundation model for eeg." arXiv preprint arXiv:2311.03764 (2023).
Chen, Yuqi, et al. "EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model." arXiv preprint arXiv:2401.10278 (2024).
Electroencephalography (EEG) is a fundamental tool in neuroscience, allowing us to monitor the brain's electrical activity non-invasively. It is beneficial for diagnosing and treating neurological disorders, but interpreting EEG data can be tricky due to many factors, such as low signal-to-noise ratio and the inherent complexity of EEG signals.
Large Language Models (LLMs), such as OpenAI’s GPT series, are a specific type of Foundational Model designed to understand, generate, and manipulate human language. LLMs are trained on extensive collections of text data, allowing them to learn a wide range of language patterns and nuances. This training enables them to perform various language-related tasks, from simple text generation to more complex applications like summarization, translation, and answering questions across multiple domains.
Foundational Models, a broader category, include LLMs and models designed for other data types, such as images, audio, and time-series. The common thread among all Foundational Models is their training approach: they are typically pre-trained on a large, diverse set of data to develop a broad understanding of a particular type of input, whether text, visual content, or EEG signals. After this extensive pre-training phase, these models are fine-tuned on more specific datasets or tasks to adapt their capabilities to more specialized applications.
Foundation models, known for their extensive pre-training on large datasets before being fine-tuned for specific tasks, have dramatically altered the landscape in natural language processing and computer vision. Nevertheless, their application in interpreting the complexities inherent in EEG data is still developing. This project proposes to develop a sophisticated foundational model specifically for EEG analysis, harnessing the capabilities of deep learning and AI.
Our project aims to make EEG analysis more accessible and accurate using the power of artificial intelligence. We plan to build a specialized foundational model for EEG data in this project.
Here is what we are going to do: 1. **Dataset Compilation**: We will gather various open-source EEG datasets. We will focus on ensuring the data is diverse, covering different demographics, conditions, and ways the data was collected.
2. **Model Architecture Design**: Next, we will develop a neural network architecture tailored for EEG data. We’ll take inspiration from successful models in other areas and adapt them to meet our needs.
3. **Pretraining**: We will train our model using the datasets we've compiled. We'll use transfer and semi-supervised learning to make the most of our data.
4. **Fine-Tuning**: We will fine-tune our model using a smaller, more specific set of data for tasks like detecting seizures or classifying sleep stages.
5. **Validation and Testing**: Finally, we will test our model to see how well it performs. We'll use metrics like accuracy, precision, recall, and the F1-score to evaluate and compare it to existing methods.
**Requirements**
Strong programming skills in Python
Machine Learning background (courses etc.)
**Related literature**
Jiang, Wei-Bang, Li-Ming Zhao, and Bao-Liang "Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI" ICLR 2024
Wang, Christopher, et al. "BrainBERT: Self-supervised representation learning for intracranial recordings." ICLR 2023.
Cui, Wenhui, et al. "Neuro-gpt: Developing a foundation model for eeg." arXiv preprint arXiv:2311.03764 (2023).
Chen, Yuqi, et al. "EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model." arXiv preprint arXiv:2401.10278 (2024).
Our goal is to make EEG analysis not only better but also easier for researchers and clinicians. This project is about bringing practical, effective AI solutions into the world of neuroscience to help improve how we understand and treat the human brain.
Our goal is to make EEG analysis not only better but also easier for researchers and clinicians. This project is about bringing practical, effective AI solutions into the world of neuroscience to help improve how we understand and treat the human brain.
Please include your CV and transcript in the submission.
**Thorir Mar Ingolfsson**
https://thorirmar.com
thoriri@iis.ee.ethz.ch
**Yawei Li**
https://yaweili.bitbucket.io/
yawei.li@vision.ee.ethz.ch
**Xiaying Wang**
https://xiaywang.github.io/
xiaywang@iis.ee.ethz.ch
Please include your CV and transcript in the submission.
