¿Qué ocurre en el cerebro en el segundo previo a un impulso?
What happens in the brain in the second before an impulse?
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Saṃdhi (संधि) — in Sanskrit, the point of union or transition between two states. Here: the neural window between an impulse and a conscious action.
Sandhi Interface is an independent applied neurotechnology research initiative exploring whether the Readiness Potential — the pre-conscious neural signature of an impending voluntary action — can serve as a measurable marker of pre-impulse states in people with impulse control disorders.
Can the Readiness Potential (RP) — detectable via EEG up to 2 seconds before conscious intent — be used to characterize pre-impulse neural states in conditions like anxiety, eating disorders, compulsive behavior, and self-harm?
Impulse control disorders share a common mechanism:
internal state → impulse → automatic action → loss of behavioral control
Clinical intervention almost always occurs after the behavior. Yet the neuroscience shows the brain prepares actions before conscious awareness of intent. The pre-impulse window is measurable. Sandhi Interface is measuring it.
| Year | Finding | Relevance |
|---|---|---|
| 1965 | Kornhuber & Deecke — Bereitschaftspotential | Slow cortical negativity beginning 1–2 s before voluntary movement, originating in the supplementary motor cortex. |
| 1983 | Libet et al. — conscious intent & motor preparation | Brain activity predicts felt "urge to move" by several hundred ms — revealing the unconscious initiation of voluntary acts. |
| 2005 | Kessler et al., NCS-R | 1 in 4 people worldwide will experience an impulse-related disorder in their lifetime. |
| 2008 | Haynes et al. — predictive decoding | Decision outcomes decoded from fMRI 7–10 s before conscious awareness, extending the pre-conscious window dramatically. |
Our hypothesis: Impulsive states manifest as detectable patterns of motor preparation — measurable via EEG — that differ significantly from controlled or inhibited responses.
Hardware — Neuroelectrics Enobio 8: wireless, 8-channel EEG, 24-bit resolution, 500 Hz sampling rate.
Electrode placement — Cz FCz Fz C3 C4 F3 F4 Pz — targeting primary motor cortex (M1), supplementary motor area, and prefrontal cortex.
Electrode modalities — Wet gel (Ag/AgCl) and dry gel electrodes. Active comparison of signal quality across both.
Software stack — NIC2 for acquisition · MNE-Python for analysis · Python pipeline (scipy, matplotlib, numpy)
Paradigms
| Condition | Code | Description |
|---|---|---|
| Rest Eyes Open | EO |
Baseline resting state |
| Rest Eyes Closed | EC |
Baseline resting state |
| Motor Imagery | MI |
Imagined movement, no physical execution |
Experimental phases
| Phase | Name | Description |
|---|---|---|
| I | Spontaneous RP characterization | Free-movement paradigm. Baseline acquisition and pipeline validation. |
| II | Go / No-Go inhibitory control | Compare pre-motor activity between executed and inhibited responses. |
| III | Impulsivity EEG markers | Differentiate neural patterns preceding controlled vs. impulsive actions. |
Signal analysis strategy — temporal segmentation · spectral power analysis · SCP/RP extraction · cross-condition comparison
sandhi-interface/
├── edf_conv.py ← batch format comparison + reporting
├── easy_edf_graph.py ← visual + statistical deep dive (single pair)
├── requirements.txt
├── docs/ ← GitHub Pages site
│ ├── index.html
│ └── sandhi-logo.png
├── data/ ← raw EEG sessions (not tracked in git)
├── comparison_reports/ ← analysis outputs
└── research_log/ ← chronological field notes
Batch pairwise comparison of .easy / .edf file pairs from a NIC2 session directory. Auto-detects .easy-only batches and falls back gracefully.
Outputs: *_channels.csv · *.json · *.md
Deep visual and statistical comparison for a specific file pair. Signal overlays, power spectra, per-channel stats, heatmap.
Outputs: *_raw_overlay.png · *_demeaned_overlay.png · *_histograms.png · *_stats_heatmap.png · *.csv · *.json
git clone https://github.com/binivazqua/eno_love.git
cd eno_love
python -m venv eno_venv
source eno_venv/bin/activate
pip install -r requirements.txt./eno_venv/bin/python edf_conv.py \
--data-dir data/session_XXX \
--output-dir comparison_reports/session_XXX./eno_venv/bin/python easy_edf_graph.py \
--easy data/session_XXX/recording.easy \
--edf data/session_XXX/recording.edf \
--info data/session_XXX/recording.info \
--output-dir comparison_reports/session_XXX_graphs| Signal | Interpretation |
|---|---|
mean_correlation → 1.0 + rmse → 0 in .easy-only mode |
Format consistency confirmed — not a physiological quality metric. |
Stable sfreq and duration across sessions |
Good acquisition stability. |
Outlier std or peak-to-peak in a channel |
Check electrode contact; candidate for exclusion. |
| Consistent metrics across condition repetitions | Reproducible paradigm execution. |
find data/session_XXX -type f \( -name '*.easy' -o -name '*.edf' -o -name '*.info' \) | sort
wc -l data/session_XXX/*.easy
# Files at 0 lines or 0B → exclude and flag for re-recordingBiniza Verónica Vázquez Moreno — Principal Investigator
Robotics and Systems Engineering · Tecnológico de Monterrey Campus Puebla
Premio Mujer Tec 2026 · g.tec BR4IN.IO Spring School 2026 (€1,000 prize)
Contributors
- Linda Michelle Silva Ramos — Research Interface Specialist · Mechatronics Engineering (8th semester) · Physical & UI interface for RP measurement
- Gianluca de la Rosa Bandini — Research Interface Specialist · Mechatronics Engineering (8th semester) · Physical & UI interface for RP measurement
- Sofía Navarro Rebolledo — Human Factors Research Assistant · Future neuroscientist · High school senior
Advisors
- Dr. Ana Luisa Santaolalla — Harvard Neuroscience · Hult MBA
- Yolanda Fajardo — Clinical Psychologist, Founder of Serena Mente Puebla
- Dr. Christoph Guger — Founder & CEO, g.tec biomedical engineering
This research is entirely self-funded and independently conducted.
Fund the next hardware → GoFundMe (coming soon)
We won €1,000 at g.tec BR4IN.IO Spring School 2026 — a real start. Our crowdfunding goal is the Unicorn Hybrid Black (g.tec): active dry electrodes, higher channel density, purpose-built for RP research. Every contribution gets us closer.
Collaborate → Open an issue
Backgrounds welcome: neuroscience, clinical psychology, robotics, signal processing. Replication, protocol review, and co-authorship conversations are open.
Use the tools → If you work with Neuroelectrics hardware, these scripts are yours. PRs welcome.
- Kornhuber, H.H., & Deecke, L. (1965). Bereitschaftspotential und reafferente Potentiale. Pflügers Archiv, 284, 1–17.
- Libet, B., Gleason, C.A., Wright, E.W., & Pearl, D.K. (1983). Time of conscious intention to act in relation to onset of cerebral activity. Brain, 106(3), 623–642.
- Haynes, J.D., et al. (2008). Reading hidden intentions in the human brain. Current Biology, 17(4), 323–328.
- Kessler, R.C., et al. (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the NCS-R. Archives of General Psychiatry, 62(6), 593–602.
- Uhlhaas, P.J., & Singer, W. (2006). Neural synchrony in brain disorders. Neuron, 52(1), 155–168.
- Gramfort, A., et al. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7, 267.
"The readiness potential begins long before we feel the intention to move.
What does the brain know that the mind hasn't said yet?"
Sandhi Interface · Independent EEG Research · Puebla, México