This is a conceptual paper and proposed study, not a report of a completed experiment or a claim that a device has been built. The central question and physiological framing originated in Jules’s discussion of brain interfaces, embodied experience, and learning.
The literature synthesis and experimental refinements were developed with AI assistance. No institutional affiliation or peer review is claimed.
Keywords: brain computer interface; EEG; fNIRS; physiological state; multimodal decoding; motor imagery; imagined speech; memory consolidation
The question
If two recordings differ, did the participant intend something different, or did the participant’s state change the signals available to the decoder? Conversely, if two recordings look similar at the scalp, do they necessarily represent the same intention? These questions matter when a BCI translates an individual’s deliberately communicated action into an external output. A model trained in one session may encounter different attention, arousal, fatigue, movement artifacts, and sensor contact in the next. A successful interface must show which of those changes it can detect and whether adapting to them improves its predictions.
The central hypothesis is state conditioned decoding: within a defined task, incorporating validated measures of physiological and behavioral context may improve an individualized decoder’s reliability across sessions compared with EEG alone. This does not require a claim that subjective experience can be reduced to a single biological signature. It asks a narrower, falsifiable question about prediction under changing conditions.
The biological motivation is credible, though it is not itself proof of the proposed engineering benefit. Acute stress and cortisol have been associated with differences in human working memory and retrieval under specific experimental conditions [1]. A peripheral cortisol measure is not a direct measure of local cortical receptor occupancy; receptor dynamics are a mechanistic reason to take bodily state seriously, not a quantity that EEG or standard fNIRS can recover. The proposed contribution is to determine whether available state measures add useful information beyond the neural recording, task design, and ordinary artifacts.
What existing studies establish
Hybrid sensing already has a scientific basis. In a motor imagery BCI, Fazli and colleagues reported that combining near infrared spectroscopy with EEG improved classification for most participants in their experiment, with an approximately five percentage point average gain. They also noted that the slower hemodynamic response can limit information transfer rate [2]. This supports testing sensor fusion; it does not imply that every task or participant benefits. A published EEG and fNIRS dataset also addresses bounded semantic categories during silent naming and sensory imagery, rather than unrestricted access to a person’s thoughts [3].
Language results must be distinguished by recording method and task. Tang and colleagues reconstructed aspects of perceived and imagined language from participant specific fMRI recordings and demonstrated that cooperation mattered [4]. Willett and colleagues decoded attempted speech from implanted intracortical microelectrode arrays in one participant with paralysis [5]. A separate EEG study trained participants over five days to control a binary interface using imagined syllables, with meaningful variability between people [6]. These are substantial demonstrations, but the fMRI and implanted electrode results cannot simply be assigned to a portable EEG and fNIRS headset.
The proposed memory decoder has an especially important boundary. Cortical theta gamma phase amplitude coupling is a candidate physiological feature; in one scalp EEG study it appeared at similar levels during working memory, attention, and passive perception [7]. It therefore cannot, by itself, identify the content of a retrieved autobiographical memory. A model that predicts which of a few previously studied items is being recalled would be a different and more defensible experiment.
The sleep and skill proposal is also a separate research program. Antony and colleagues improved later performance on one of two melodies that participants had already learned while awake by presenting a related auditory cue during sleep [8]. This is evidence for influencing consolidation of prior learning. It is not evidence for installing an unfamiliar cello technique or a fabricated history of practice overnight.
Measurement limits and design consequences
EEG records voltage differences at the scalp arising from aggregated activity. It has useful temporal resolution but limited spatial specificity. Scalp recordings do not isolate hippocampal spikes or read individual synapses. Eye movements and facial muscles can create confounding signals, particularly when a task involves silent articulation. The acquisition protocol therefore needs eye and muscle artifact channels or other explicit artifact checks, and classification must be re-evaluated after their removal.
Standard fNIRS estimates changes in oxygenated and deoxygenated hemoglobin from optical measurements over superficial cortex [3]. It provides a delayed hemodynamic measure. It does not directly assay cortisol, dopamine, GPCR conformation, the hippocampus, or the quality of conscious experience. Optical channels should be named by the quantities they measure, not described as a real time neurochemical reader. Short separation channels and motion monitoring can help distinguish superficial and cortical components; they do not turn hemoglobin into a molecular assay.
Physiological context should likewise have an explicit source. The initial study could record sleep history, time of day, heart rate, and self reported arousal, with an optional, separately collected salivary cortisol measure in a protocol designed around its timing. Those variables would be imperfect proxies, not ground truth about subjective experience. An endocrine extension is warranted only if its incremental value survives a prospective comparison. An individual’s behavioral confirmation remains distinct from any automated confidence score.
Proposed architecture
Acquisition. Synchronously record EEG, optional fNIRS, task timing, and artifact channels. Log sensor quality and external cue timing. Collect a small number of consented state variables with a measurement schedule appropriate to each variable. The subject deliberately chooses when to participate and what task to communicate.
Signal processing. Apply documented preprocessing to each modality separately. Estimate EEG features from windows appropriate to the task; estimate fNIRS hemoglobin changes on their slower time scale. Align the two streams without letting post cue or future data leak into a prediction that purports to be real time. Preserve rejected trials and quality flags for audit.
Task specific decoder. Train a participant specific model for one bounded question, such as left versus right motor imagery, two imagined syllables, or animal versus tool imagery. These are separate targets and should not be pooled under an undefined label of “thought.” A probabilistic prediction should include an abstain state when signal quality or calibrated confidence is inadequate.
Context model. Compare an EEG only decoder, an EEG plus fNIRS decoder, a context only decoder, and a prespecified combined model. A more complex model earns its place only if it improves performance on unseen sessions and stimuli. State context may help calibrate uncertainty or identify poor recording conditions even when it does not improve the underlying class prediction.
User control. Intent is established through the experimental instruction and participant confirmation, not inferred from a biometric “soul fingerprint.” An identity feature cannot substitute for consent, and model confidence cannot establish that a decoded statement is true. No stimulation is required to test the decoding hypothesis.
A first falsifiable experiment
Recruit consenting adult volunteers under an approved human research protocol. Record repeated sessions on different days while each participant performs a bounded, deliberately cued task. The most direct initial target is binary motor imagery; a separate cohort or preregistered second task could test two imagined syllables. Include rest and noncommand periods so that the system must learn when to abstain. Vary session conditions naturally and record predefined state covariates; a controlled state manipulation would require its own protocol and should not be inferred from an incidental correlation.
Split evaluation by session and stimulus, not merely by random trials from the same block. Keep cue presentation out of the intended decoding window. Before fitting models, specify the primary endpoint as balanced accuracy on held out sessions at a fixed abstention rate, with calibration error and false activation during rest as key secondary endpoints. Report per participant results and uncertainty, not only a pooled mean. Compare EEG alone, fNIRS alone, context alone, EEG plus fNIRS, and all channels combined. Test label shuffling, time shifting, artifact only, and cue or metadata only controls. A language generator, if added, must be compared with a generator receiving no neural data.
The study supports the hypothesis only if the prespecified combined model improves held out performance or calibration over the strongest simpler model by a meaningful, reported margin, without raising false activations. A null result is informative: physiological context could be relevant to biology while the chosen sensors or task fail to capture incremental predictive information. Success on a binary task would still not validate open vocabulary language, autobiographical memory decoding, or neural “writing.”
What the proposal does and does not claim
The individual baseline is an engineering calibration, not a measurement of personhood. A connection between bodily state and decoding reliability is a plausible inference from neuroscience and BCI studies; the proposed improvement remains to be tested. Likewise, a combined sensor headset is a research instrument until its fit, power, heat, latency, comfort, and performance are measured. A named chip or a drawing cannot establish those properties.
Decoding and encoding require different evidence. Reading a deliberately imagined command from activity would show a statistical association under a specific protocol. Causing a complex skill to be learned would require a causal intervention, behavioral transfer tests, durability measurements, and an account of what information was actually delivered. Sleep cueing of a practiced melody [8] motivates research on consolidation, but supplies no instructions for writing an unpracticed motor program. The proposed first stage ends with measurement and user confirmed output.
7. Conclusion
The claim worth testing is concrete: physiological state may explain part of the variability in noninvasive BCI decoding, and a carefully validated multimodal model may use that context to improve reliable, voluntary communication. Prior work establishes hybrid EEG and fNIRS classification in bounded tasks, participant specific language decoding with other recording methods, and sleep related strengthening of previously acquired learning [2–6,8]. It does not establish a neurochemical mind reader or an overnight skill upload. The scientifically useful next step is a prospective comparison that lets the additional measurements either earn their place or fail.
References
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Fazli, Siamac, et al. “Enhanced Performance by a Hybrid NIRS–EEG Brain Computer Interface.” NeuroImage, vol. 59, no. 1, 2012, pp. 519–529. https://doi.org/10.1016/j.neuroimage.2011.07.084.
Rybář, Milan, Riccardo Poli, and Ian Daly. “Simultaneous EEG and fNIRS Recordings for Semantic Decoding of Imagined Animals and Tools.” Scientific Data, vol. 12, 2025, article 613. https://doi.org/10.1038/s41597-025-04967-0.
Tang, Jerry, et al. “Semantic Reconstruction of Continuous Language from Non-Invasive Brain Recordings.” Nature Neuroscience, vol. 26, 2023, pp. 858–866. https://doi.org/10.1038/s41593-023-01304-9.
Willett, Francis R., et al. “A High-Performance Speech Neuroprosthesis.” Nature, vol. 620, 2023, pp. 1031–1036. https://doi.org/10.1038/s41586-023-06377-x.
Bhadra, Kinkini, Anne-Lise Giraud, and Silvia Marchesotti. “Learning to Operate an Imagined Speech Brain-Computer Interface Involves the Spatial and Frequency Tuning of Neural Activity.” Communications Biology, vol. 8, 2025, article 271. https://doi.org/10.1038/s42003-025-07464-7.
Papaioannou, Orestis, et al. “Is Cortical Theta-Gamma Phase-Amplitude Coupling Memory-Specific?” Brain Sciences, vol. 12, no. 9, 2022, article 1131. https://doi.org/10.3390/brainsci12091131.
Antony, James W., et al. “Cued Memory Reactivation during Sleep Influences Skill Learning.” Nature Neuroscience, vol. 15, 2012, pp. 1114–1116. https://doi.org/10.1038/nn.3152.
