The proposed framework that integrates Vision Transformer 3D with fMRI data and Large Language Models (LLMs) can be extended to decode and interact with other modalities of brain data, such as EEG (Electroencephalography) or MEG (Magnetoencephalography), by adapting the feature extraction and alignment processes.
Feature Extraction: For EEG and MEG data, the feature extraction process would need to be tailored to the specific characteristics of these modalities. EEG data, for example, captures electrical activity in the brain, while MEG data measures magnetic fields. The feature extractor would need to be designed to extract relevant features from these different types of data.
Alignment with LLMs: Just as fMRI features are aligned with visual embeddings and VAE features in the current framework, EEG or MEG features would need to be aligned with appropriate representations in the LLMs. This alignment process would enable the model to interpret and generate language-based outputs from the EEG or MEG data.
Training and Fine-Tuning: The model would need to be trained and fine-tuned using datasets that include EEG or MEG data along with corresponding language annotations. This training process would help the model learn the relationships between the brain data and the language outputs.
Integration of Multiple Modalities: To decode and interact with multiple modalities simultaneously, the framework could be expanded to incorporate fusion techniques that combine information from different types of brain data. This integration would enable a more comprehensive understanding of cognitive processes by leveraging the strengths of each modality.
In summary, extending the proposed framework to decode and interact with EEG or MEG data would involve customizing the feature extraction, alignment, training, and integration processes to suit the characteristics of these modalities, ultimately enhancing the model's ability to interpret and generate language-based outputs from diverse brain data sources.