MindSpeech: Continuous Imagined Speech Decoding using High-Density fNIRS and Prompt Tuning for Advanced Human-AI Interaction (2408.05362v1)

Abstract: In the coming decade, artificial intelligence systems will continue to improve and revolutionise every industry and facet of human life. Designing effective, seamless and symbiotic communication paradigms between humans and AI agents is increasingly important. This paper reports a novel method for human-AI interaction by developing a direct brain-AI interface. We discuss a novel AI model, called MindSpeech, which enables open-vocabulary, continuous decoding for imagined speech. This study focuses on enhancing human-AI communication by utilising high-density functional near-infrared spectroscopy (fNIRS) data to develop an AI model capable of decoding imagined speech non-invasively. We discuss a new word cloud paradigm for data collection, improving the quality and variety of imagined sentences generated by participants and covering a broad semantic space. Utilising a prompt tuning-based approach, we employed the Llama2 LLM for text generation guided by brain signals. Our results show significant improvements in key metrics, such as BLEU-1 and BERT P scores, for three out of four participants, demonstrating the method's effectiveness. Additionally, we demonstrate that combining data from multiple participants enhances the decoder performance, with statistically significant improvements in BERT scores for two participants. Furthermore, we demonstrated significantly above-chance decoding accuracy for imagined speech versus resting conditions and the identified activated brain regions during imagined speech tasks in our study are consistent with the previous studies on brain regions involved in speech encoding. This study underscores the feasibility of continuous imagined speech decoding. By integrating high-density fNIRS with advanced AI techniques, we highlight the potential for non-invasive, accurate communication systems with AI in the near future.

• The paper presents the MindSpeech BCI model, using high-density fNIRS and Llama2 prompt tuning to decode continuous imagined speech for human-AI interaction.
• Results show the model achieved 76% accuracy differentiating imagined speech from rest and improved decoding performance by incorporating context and multi-participant data.
• The findings suggest potential for intuitive human-AI interfaces, especially for individuals with speech impairments, though further work is needed for real-time robustness.

An Evaluation of MindSpeech: Utilizing fNIRS for Imagined Speech Decoding in Human-AI Interfaces

The paper "MindSpeech: Continuous Imagined Speech Decoding using High-Density fNIRS and Prompt Tuning for Advanced Human-AI Interaction" presents a novel brain-computer interface (BCI) model termed "MindSpeech." This model focuses on decoding imagined speech using non-invasive brain imaging data collected via high-density functional near-infrared spectroscopy (fNIRS). It integrates LLMs, specifically Llama2, to facilitate seamless communication between human users and AI systems. The paper systematically explores the construction and evaluation of this imagined speech decoder, leveraging advanced machine learning techniques to enhance its performance.

Methodological Overview

The primary objective of the paper is to decode imagined speech with an open vocabulary, moving beyond the constraints of prior models that primarily focused on predefined or limited semantic spaces. The authors chose high-density fNIRS as the imaging modality due to its comparative advantages in terms of portability and cost-effectiveness over methods like fMRI while maintaining a considerable spatiotemporal resolution.

The paper's approach is centered around a newly designed "word cloud" paradigm for data collection. Participants imagined sentences based on displayed topic words and related keywords. This setup allowed for the capture of a wide range of semantic meanings and eliminated confounding factors related to memorization or external auditory processing, which are often present in other paradigms. The imagined sentences were typed out post-completion to serve as ground truth for training the decoder.

Integration with LLMs

A central innovation detailed in the paper is the application of prompt tuning techniques, utilizing the Llama2 LLM to decode brain signals into natural text. The model bypasses traditional post-hoc candidate selection by directly generating continuous speech from semantic brain representations. This is achieved through a sequence-to-sequence (Seq2Seq) neural network model using transformers, which accommodates the higher temporal resolution of fNIRS data compared to fMRI.

Results and Performance

The paper reports on tests conducted with both individual and multi-participant datasets. The individually trained models exhibited statistically significant improvements in BLEU-1 and BERT P scores for some participants when context inputs were combined with brain signal data, compared to permutation conditions. The authors demonstrated that using data from multiple participants further improved performance, showcasing the model's capability to generalize across different semantic mappings within the brain. However, they also discussed variability in results due to individual differences in linguistic habits and neural signal patterns, especially for certain participants.

The classification accuracy of separating imagined speech from rest conditions using an Extra Trees Classifier reached an average of 76%, indicating the model's proficiency in identifying active brain states associated with covert speech.

Theoretical and Practical Implications

This paper contributes theoretically to the field by advancing the integration of neural BCI with generative LLMs, highlighting the potential of non-invasive techniques to decode complex neural representations. Practically, such advancements in imagined speech decoding facilitate the design of more intuitive human-AI interaction systems, which could be particularly beneficial for individuals with speech impairments.

Future Directions

As suggested by the authors, further research is needed to increase decoder robustness, with efforts likely focused on expanding training datasets and refining brain signal preprocessing techniques to reduce inherent noise. The ultimate goal is to develop a real-time application to decode imagined speech as it occurs, which will require reduced computational demands and enhanced system efficiencies.

Conclusion

Overall, "MindSpeech" presents an intriguing synthesis of BCI and LLM strategies for imagined speech decoding. While the system shows promise, particularly in enhancing communication interfaces, the findings underscore the challenges posed by variability in brain activation patterns. Continued exploration and development are crucial to unlocking the full potential of this technology in advancing user-centric AI systems.