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Integrating Language Models into Listened and Imagined Speech Decoding from MEG
Maryam Maghsoudi, Sai Samrat Kankanala, Shihab A. Shamma, Sriram Ganapathy
TL;DR
Imagined-speech decoding remains difficult, and it is unclear whether language-model priors help it differently from listened-speech decoding. The paper combines contrastive MEG-decoder training with neural-constrained beam search and finds that imagined speech benefits more from language-model evidence, with its best balance shifted toward that evidence.
Problem
The benefit of language-model priors for imagined speech, and whether it differs from listened speech as neural evidence varies in reliability, remains unclear.
Source: §1 · p. 1Method
The paper trains a contrastive MEG decoder against acoustic and contextual language representations and combines neural scores with language-model next-word probabilities during neural-constrained beam search.
Source: §1 · p. 1, §2.3 · p. 3Results
Takeaways & Limitations
The findings suggest language priors may be particularly valuable for imagined-speech decoding, where neural evidence is weaker.
Source: §1 · p. 1Evaluation is limited to two poems, a closed vocabulary, 13 trained musicians, and known word boundaries, and does not test unseen stimuli or continuous speech.
Source: §4 · p. 4
Abstract
from arXiv · showhide
Decoding imagined speech is an important goal for brain-computer interfaces but remains challenging due to weak neural responses, low signal-to-noise ratio, and limited imagined-speech datasets. Language models provide strong contextual cues for text prediction, but how much they can help neural decoding and whether their contribution differs for decoding perceived and imagined speech remains unclear. To investigate this, we use a paired listened-imagined MEG dataset and incorporate language-model information at two stages. First, we train a contrastive neural decoder that aligns MEG representations with acoustic and contextual language representations, improving cross-subject word decoding for both listened and imagined speech. Second, at inference, we introduce a neural-constrained beam-search framework that combines neural evidence with language-model next-word probabilities. We find that imagined-speech decoding benefits more from the language model than listened-speech decoding. For Imagined speech, the best-performing balance between neural and language-model evidence shifts toward the language model, and the gain over neural-only decoding is larger. Together, these results suggest that language priors are most useful when neural evidence is weaker, making them particularly valuable for imagined-speech BCIs.
1. INTRODUCTION
The paper asks whether language-model priors help listened and imagined speech decoding differently, given that imagined speech has weaker, less reliable neural responses. It introduces contrastive training and neural-constrained beam search, finding that imagined speech favors more language-model contribution and gains more over neural-only decoding.
- Imagined speech is harder to decode because its neural responses are weaker and less reliably time-locked, and its datasets are relatively small.The lack of directly observable output and difficulty measuring timing contribute to limited imagined-speech datasets.Source: §1 · p. 1
- Although language priors have supported neural speech reconstruction, their benefit for imagined speech and its relation to neural-evidence reliability remain unclear.The paper specifically asks whether listened and imagined speech benefit differently.Source: §1 · p. 1
- The study examines how language-model contributions differ between listened and imagined speech using a paired MEG dataset and two integration stages.Language information is incorporated during contrastive decoder training and at inference through next-word probabilities.Source: §1 · p. 1
- Imagined speech performs best with a greater language-model contribution and gains more over neural-only decoding than listened speech.The results suggest language priors become more valuable as neural evidence becomes less reliable.Source: §1 · p. 1
2. METHODS
The methods use a paired listened–imagined MEG dataset with leave-one-subject-out evaluation, train a decoder to align MEG with acoustic and contextual representations, and combine neural and language-model scores during inference. Fusion weights, candidate sets, beam widths, and controls are evaluated to compare the contributions of neural and language-model evidence.
2.1. Dataset
- The dataset contains 17 participants who listened to or imagined two repeated poems; four participants were excluded, leaving 13 for analysis.Imagined responses are mapped to listened responses using six mapping variants; decoder training and evaluation follow leave-one-subject-out splits.Source: §2.1 · p. 2
2.2. Contextual Contrastive Decoder
- A shared MEG encoder is trained with frame-level wav2vec 2.0 and word-level GPT-2 InfoNCE objectives, with the training weight shifting from acoustic to contextual alignment.The encoder is trained on listened trials; GPT-2 targets use preceding poem context, while the encoder receives only raw MEG.Source: §2.2 · p. 2
2.3. Language-Model Fusion and Neural-Constrained Beam Search
- At inference, normalized neural and language-model scores are interpolated, with the neural decoder restricting GPT-2 scoring to its top-k candidate words during beam search.The language model scores candidates against each beam’s decoded history, and the B highest-scoring sequences are retained.Source: §2.3 · pp. 2–3
2.4. Evaluation, Fusion Sweep, and Controls
- Evaluation uses word accuracy across held-out subjects and sweeps fusion weight α, beam width, and candidate-set size against random-candidate and LLM-only controls.The figure reports accuracy versus α for teacher forcing and beam search; beam-search curves peak at intermediate α, with separate panels for listened and mapped imagined MEG.Source: §2.4 · p. 3, Figure 2 · p. 3
3. RESULTS
Neural-constrained language-model fusion improved decoding in both conditions, but mapped imagined MEG favored a larger language-model contribution and gained more over MEG-only ranking than listened MEG. The balance also depended on inference conditions and candidate-set size.
Imagined Speech Shifts the Optimal Balance Toward the Language Model
- p < 0.01 for higher optimal α* across k, and p < 0.001 for larger gain over MEG-only, in mapped imagined versus listened MEG.Fig. 3 shows the selected α* and relative gain comparisons.Source: Figure 3 · p. 4, §3 · p. 4
Inference-Time Fusion Balances Neural and Language Evidence
- With teacher forcing, accuracy rose as α approached 1 and the conditions converged, but this bounds context benefit because true preceding words are unavailable at inference.With that correct history, the language model dominated ranking and neural evidence added little.Source: §3 · p. 4
- Joint neural–language scoring improved over MEG-only in both conditions (p < 0.001) and stayed above the random-candidate null; listened stayed above LLM-only throughout, while imagined did so near its optimum.Neural-constrained beam search ranks only candidates proposed by MEG.Source: §3 · p. 4
- At α = 0, MEG-only ranking outperformed LLM-only for listened MEG, but the order reversed for mapped imagined MEG; neither endpoint was optimal.Accuracy peaked between the endpoints, at a different α in each condition.Source: §3 · p. 4
Imagined Speech Shifts the Optimal Balance Toward the Language Model
- For every k > 1, accuracy peaked at intermediate α; as k grew, removing relative MEG scores became more costly because MEG both selects candidates and ranks them.Results were consistent across beam widths; the passage reports B = 3.Source: §3 · p. 4
4. DISCUSSION AND LIMITATIONS
Both conditions peaked at an intermediate language-model weight, but imagined speech favored more language contribution and gained more over neural-only decoding. The evaluation was limited in stimulus, vocabulary, participant, and boundary scope; the findings suggest adapting language context to neural reliability.
Discussion
- Imagined speech favored a larger language contribution and gained more over neural-only decoding, while both conditions peaked at an intermediate weight.The discussion interprets this pattern as consistent with language priors becoming more useful as neural evidence weakens.Source: §4 · p. 4
Limitations
- The evaluation used two poems, a closed vocabulary, 13 trained musicians, and known word boundaries; it did not test unseen stimuli or continuous speech.LOSO tested generalization across subjects, not across stimuli or continuous speech.Source: §4 · p. 4
Discussion
- Future speech BCIs may benefit from adapting language context to neural-prediction reliability, using more neural evidence when predictions are strong and more linguistic context when uncertain.Source: §4 · p. 4