Neuro-GPT: Towards A Foundation Model for EEG (2311.03764v4)

Abstract: To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch, which provides evidence for the generalizability of the foundation model and its ability to address challenges of data scarcity and heterogeneity in EEG. The code is publicly available at github.com/wenhui0206/NeuroGPT.

• The paper presents a novel model that combines an EEG encoder with GPT using self-supervised learning to extract robust spatio-temporal features.
• It employs causal masking and auto-regressive pre-training to capture temporal dependencies, significantly boosting motor imagery classification performance.
• Fine-tuning experiments highlight that the encoder-only strategy achieves the best accuracy, demonstrating the potential of pre-training on large, heterogeneous EEG datasets.

Overview of "Neuro-GPT: Towards A Foundation Model for EEG"

The paper "Neuro-GPT: Towards A Foundation Model for EEG" proposes a novel approach, termed Neuro-GPT, aimed at addressing the challenges posed by the scarcity and heterogeneity of electroencephalography (EEG) data in the context of Brain-Computer Interface (BCI) tasks. The work focuses on leveraging large-scale EEG datasets through the use of a foundation model that integrates an EEG encoder with a Generative Pre-trained Transformer (GPT) model, a strategy inspired by the success of LLMs in various domains.

Methodology and Key Contributions

The Neuro-GPT model is designed with two primary components: an EEG encoder, which is responsible for extracting spatio-temporal features from EEG signals, and a GPT model that employs self-supervised learning to predict masked segments from these extracted features. The methodology involves pre-training the model using a large dataset and subsequently fine-tuning it for specific tasks, such as motor imagery classification.

1. EEG Encoder and Self-supervised Pre-training: The encoder utilizes convolutional and transformer layers to reduce the dimensionality of EEG data and focuses on learning robust features from raw signals. Self-supervised learning is applied by masking segments of EEG data and requiring the model to predict the masked portion based solely on the preceding data chunks. This introduces the model to the temporal dependencies and variability inherent in EEG data.
2. Causal Masking and GPT Integration: Inspired by auto-regressive pre-training methods in NLP, the model incorporates causal masking, which allows the GPT model to focus on predicting the next sequence by learning from the previous data chunks. This facilitates the GPT model's ability to understand temporal patterns within the EEG data.
3. Fine-tuning on Motor Imagery Tasks: The foundation model, after pre-training, is fine-tuned on a motor imagery classification task using a small dataset from the BCI Competition IV Dataset. Three fine-tuning strategies are explored: utilizing the encoder alone, combining the encoder and GPT, and employing a linear model fine-tuned with the pre-trained encoder's features.

Experimental Results and Analysis

The experiments demonstrate that the application of the foundation model significantly enhances the classification accuracy of motor imagery tasks, particularly in scenarios with limited training data. The encoder-only approach achieved the best performance, indicating the encoder's ability to learn valuable and transferable features during the pre-training phase. The comparison against models trained from scratch and other approaches such as BENDR, a related transformer model, underscores Neuro-GPT's advantages in feature learning and task generalization.

Numerical results presented in Table 1 show that Neuro-GPT outperforms existing methods with significant improvements in classification accuracy, highlighting the model's capability to address the inter-subject variability common in EEG-based tasks. The pre-trained model also showcased better performance across several fine-tuning strategies, underscoring the efficacy of pre-training on large, diverse datasets for feature extraction.

Implications and Future Directions

The Neuro-GPT model paves the way for the creation of foundation models tailored for EEG data, akin to the developments realized in natural language processing. By effectively dealing with the issues of data scarcity and heterogeneity, the approach opens the door for improved BCI applications and wider generalization of EEG data analysis across different tasks and subjects.

Future developments may include extending the foundation model framework to encompass larger and more varied EEG datasets, refining the encoding architectures to further enhance feature extraction, and applying the model to additional neurophysiological tasks. The potential applicability of this approach to related fields, such as neural activity analysis and medical diagnostics, suggests broad utility beyond EEG-based BCI tasks. Such advancements could facilitate more robust decoding of neural signals, enhancing the interaction between humans and machines through improved BCIs and leading to wider and more practical applications of neurotechnology.