Neural Foundation Models for Low-SNR Brain Speech Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional brain-computer interfaces face challenges in decoding speech due to low signal-to-noise ratio and variability in neural array placements, brain signals, and subject-specific differences, limiting their effectiveness in enabling seamless communication.
Innovation Solution
Utilization of neural foundation models (NFMs) trained on diverse brain recordings to extract spatiotemporal features, transform them into embeddings, and predict speech or motor tasks, incorporating techniques like beam search decoding and reinforcement learning to enhance accuracy and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional decoding methods are used on noisy neural signals, then the system complexity remains low, but the decoding accuracy and speech prediction performance deteriorate
Solution Approach 1:
The neural foundation model is pre-trained on large-scale neural recording data from multiple subjects and tasks before deployment. This preliminary training enables the model to learn robust neural representations and decoding patterns that can be transferred to individual subjects, achieving high decoding accuracy without requiring complex subject-specific training procedures
Solution Approach 2:
The patent introduces neural embeddings as an intermediary representation layer between raw neural signals and speech predictions. These embeddings capture essential neural features in a compressed form, enabling accurate speech decoding while simplifying the overall system architecture by separating feature extraction from task-specific decoding
2Measurement precision
If highly customized interfaces are developed for each subject, then the decoding accuracy improves, but the time and resources required for customization increase
Solution Approach 1:
The neural foundation model is designed to be universal across multiple subjects, tasks, and recording sessions. By training on diverse data from many subjects performing various tasks, the model learns generalizable neural representations that can be applied to new subjects without extensive customization, reducing deployment time while maintaining accuracy
Solution Approach 2:
The patent creates a generalized neural decoding model that captures universal patterns across subjects, which can then be adapted to individual subjects through fine-tuning or direct application. This copying approach allows the benefits of extensive training to be transferred to individual users without requiring each subject to undergo the same lengthy training process
3Object-affected harmful factors
If non-penetrating cortical surface microelectrodes are used, then the invasiveness and safety improve, but the signal-to-noise ratio and signal quality deteriorate
Solution Approach 1:
The patent transforms the low-quality raw neural signals from non-penetrating electrodes into high-quality representations through neural embeddings. By changing the parameter space from raw voltage signals to learned embedding representations, the system compensates for the lower signal-to-noise ratio inherent in non-invasive recording methods
Solution Approach 2:
The patent replaces the need for high-quality physical signal acquisition (which would require penetrating electrodes) with a computational signal processing approach. The neural foundation model performs sophisticated signal processing and feature extraction that compensates for the limitations of non-penetrating electrodes, substituting mechanical/invasive signal acquisition with intelligent computational processing
4Adaptability or versatility
If the neural foundation model is trained on diverse data from multiple subjects and tasks, then the model generalizability improves, but the training data requirements and computational resources increase
Solution Approach 1:
The training process is segmented into distinct phases: pre-training on large-scale diverse data to learn general neural representations, followed by optional fine-tuning on subject-specific data. This segmentation allows the model to achieve high generalizability through pre-training while reducing the amount of subject-specific data needed, as the model has already learned robust features from the diverse pre-training data
Data Source
AI summary
A method and system for decoding speech based on recorded brain signals is provided. The method can include receiving recorded brain signals via a microelectrode array. The method can include extracting one or more features from the recorded brain signals. The method can include converting the one or more extracted features into one or more feature embeddings. The method can include transforming, by one or more encoders, the one or more feature embeddings. The method can include predicting, by one or more decoders, phonemes based on the one or more transformed feature embeddings. The method can include predicting speech based on the predicted phonemes.