Una interfaz cerebro-computadora es una vía de comunicación directa entre el tejido neural y un sistema computacional externo, que evita los canales de salida neuromusculares convencionales para traducir la actividad cerebral registrada en comandos para el dispositivo, voz sintetizada o retroalimentación sensorial restaurada en tiempo real.
El campo se divide a lo largo de un eje de invasividad: modalidades no invasivas - EEG del cuero cabelludo, infrarrojo cercano funcional espectroscopia - ofrecen la adquisición de señales sin riesgo quirúrgico pero con una resolución espacial y una relación señal/ruido muy limitadas; las rejillas de electrocorticografía colocadas en la superficie cortical ocupan un nivel intermedio; y los enfoques totalmente intracorticales, tipificados por la matriz de Utah y el sistema de electrodos de hilo flexible de Neuralink, registran simultáneamente los picos de una sola unidad de cientos a miles de neuronas a costa de la implantación quirúrgica, la respuesta de cuerpos extraños y la degradación de la estabilidad de los electrodos a largo plazo.
Procesamiento de señales oleoductos — pico clasificación, local field potential decomposition, and increasingly deep learning decoders trained on neural population dynamics — translate raw electrophysiology into high-dimensional control signals, with motor cortex decoding for cursor control and robotic limb actuation and speech area decoding for imagined or attempted speech synthesis representing the two most clinically advanced application tracks. Circuito cerrado Las arquitecturas que combinan el registro neuronal con la neuroestimulación cortical o periférica programada con precisión están impulsando simultáneamente la rehabilitación de los accidentes cerebrovasculares, el tratamiento de la depresión resistente al tratamiento y el manejo de la epilepsia.
Las publicaciones y patentes indexadas a continuación abarcan la ciencia de los materiales de electrodos, el diseño de amplificadores ASIC front-end, algoritmos decodificadores, telemetría neural inalámbrica, estudios de biocompatibilidad y ensayo clínico resultados en todo el espectro de invasividad:
This is our latest selection of worldwide publications and patents in english on Brain-Computer Interfaces (BCI), between many scientific online journals, classified and focused on BCI, brain-computer interface, neural interface, electrocorticography, intracortical electrode array, Utah array, Neuralink implant, stentrode BCI, EEG-based BCI, motor cortex decoding, neural spike sorting, local field potential BCI, spiking neural network decoder, neural signal amplifier, closed-loop neurostimulation, BCI motor neuroprosthesis, speech BCI, imagined speech decoding, BCI cursor control, BCI communication device, neural decoder algorithm, BCI artifact rejection, flexible neural probe, biocompatible neural electrode and BCI long-term stability.
Neuronal avalanches as a predictive biomarker for guiding tailored BCI training programs
Published on 2026-05-29 by @MIT
Abstract: AbstractMotor imagery-based Brain-Computer Interfaces (BCIs) restore control in persons with motor impairments, but up to 30% of users struggle, a phenomenon known as “BCI inefficiency”. This study tackles a key limitation of current protocol: the use of fixed-length sessions training paradigms that ignore individual learning variability. We propose a novel approach based on neuronal avalanches, spatiotemporal cascades of brain activities, as biomarkers to characterize and predict user-speci[...]
Our summary: This study introduces neuronal avalanches as predictive biomarkers for personalized BCI training programs. It analyzes electroencephalography data to correlate avalanche characteristics with BCI performance. The findings support tailored approaches to enhance user success and reduce BCI inefficiency.
neuronal avalanches, BCI training, biomarkers, motor imagery
Publication
Large language models reveal the neural tracking of linguistic context in attended and unattended multi-talker speech
Published on 2026-05-07 by @MIT
Abstract: AbstractLarge language models (LLMs) capture long-range contextual structure in natural language and have recently been shown to align with the human brain’s contextualized linguistic encoding. This makes them a promising computational probe for studying how context-dependent linguistic information is represented during natural speech perception. Speech perception often occurs in multi-talker environments, where attention must dynamically select among competing streams, yet how contextual info[...]
Our summary: Large language models align with human brain encoding of linguistic context. The study explores how attention affects neural tracking of speech in multi-talker environments. Findings indicate that both attended and unattended speech streams contribute to neural predictions based on contextual information.
language models, neural tracking, speech perception, auditory attention
Publication
Suppression of inflammation associated with implants
Patent published on the 2026-05-07 in WO under Ref WO2026092500 by SUNMED THERAPEUTIC LTD [CN] (Sun Joseph [cn], Sun Dongxu [cn])
Abstract: Provided herein are methods and compositions for suppressing a foreign body reaction, such as an implant-associated inflammation in a subject, by using an anti-Galectin-3 antibody. Such methods and compositions can be used in various areas applications where an implant is introduced, such as in brain-computer interface (BCI).[...]
Our summary: Methods and compositions are described for suppressing implant-associated inflammation using an anti-Galectin-3 antibody. These approaches target the foreign body reaction in subjects receiving implants. Applications include areas like brain-computer interfaces (BCI).
anti-Galectin-3, inflammation suppression, foreign body reaction, implants
Patent
Towards the use of functional near-infrared spectroscopy as an assessment tool in disorders of consciousness
Published on 2026-04-30 by @MIT
Abstract: AbstractFunctional near-infrared spectroscopy (fNIRS) has emerged as a promising neuroimaging tool for assessing patients with disorders of consciousness (DoC). While functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have advanced the detection of covert brain function, their use is often constrained by accessibility, medical and physical contraindications, and practical limitations. fNIRS offers a portable, safe, and cost-effective alternative capable of measuring he[...]
Our summary: Functional near-infrared spectroscopy (fNIRS) is a promising tool for assessing disorders of consciousness (DoC). It provides a portable and cost-effective alternative to fMRI and EEG for measuring brain function. Future research should focus on validation, multimodal integration, and ethical access to enhance DoC care.
fNIRS, disorders of consciousness, neuroimaging, brain-computer interfaces
Publication
A reference-less level-crossing adc with a bump-based adaptive-bias comparator
Patent published on the 2026-04-22 in EP under Ref EP4730654 by IMEC VZW [BE] (Yang Xiaolin [be], Xing Xiaonan [be], Sawigun Chutham [be], Mora Lopez Carolina [be])
Abstract: This disclosure relates to a comparator circuit and an ADC circuit for a neural interface. The ADC circuit includes the comparator circuit. The comparator circuit comprises a comparator to receive a first and a second signal, and internally amplify the first and the second signal. A bump bias circuit of the comparator circuit receives the amplified first and second signal, and causes the comparator to operate at a higher power level when a difference between the amplified first and second signal[...]
Our summary: The disclosure describes a reference-less level-crossing ADC featuring a bump-based adaptive-bias comparator. The comparator amplifies two input signals and adjusts its power level based on their difference. The ADC includes two comparator circuits and switching circuits controlled by a control circuit for capacitor state management.
ADC, comparator, adaptive-bias, neural interface
Patent
All spectral frequencies of neural activity reveal semantic representation in the human anterior ventral temporal cortex
Published on 2026-04-17 by @MIT
Abstract: AbstractIntracranial electrophysiology offers a unique insight into the nature of information representation in the brain—it can be used to disentangle information encoded in gamma and high gamma frequencies from information encoded in lower frequencies. We used regularised logistic regression to decode animacy from time-frequency power and phase extracted from electrocorticography (ECoG) grid electrode data recorded on the surface of human ventral anterior temporal lobe (vATL). Power in gamma[...]
Our summary: Neural activity in the anterior ventral temporal cortex encodes semantic information across various spectral frequencies. Intracranial electrophysiology reveals that gamma and high gamma frequencies contribute to reliable decoding of animacy. A broader frequency range enhances decoding accuracy, supporting the concept of a local vATL hub interacting with distributed cortical spokes.
neural activity, semantic representation, electrocorticography, frequency decoding
Publication
A novel hybrid BCI system combining single-channel SSVEP and PLR to improve classification accuracy and ITR
Published on 2026-03-24 by @OXFORD
Abstract: AbstractSteady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) have been widely studied because they provide high classification accuracy and information transfer rate (ITR) without requiring user training. To further enhance BCI performance, this study proposes a novel hybrid BCI that integrates single-channel SSVEP with the pupillary light reflex (PLR). Twelve healthy subjects participated in experiments involving three paradigms: SSVEP, PLR, and hybrid. Each sub[...]
Our summary: This study presents a hybrid BCI system that combines single-channel SSVEP and PLR to enhance classification accuracy and information transfer rate. The hybrid paradigm achieved a classification accuracy of 95.70%, outperforming both SSVEP and PLR methods. Results indicate that this approach can significantly improve BCI performance, potentially facilitating broader adoption.
BCI, SSVEP, PLR, classification
Publication
Neurophysiological screening of individual variability for robust decoding in c-VEP-based BCI
Published on 2026-03-20 by @MIT
Abstract: AbstractCode-modulated visual evoked-potential (c-VEP)-based reactive brain–computer interfaces (BCIs) deliver high information-transfer rates with minimal calibration, yet performance often collapses when models are transferred between users. We, therefore, pursue a two-fold aim: first, to pinpoint neurophysiological predictors that explain this inter-participant variability; second, to identify a decoding pipeline that sustains accuracy across users in a burst-c-VEP paradigm (brief, aperiodi[...]
Our summary: This study identifies neurophysiological predictors of inter-participant variability in c-VEP-based BCIs. It establishes a decoding pipeline that maintains accuracy across users using a lightweight approach. The proposed method achieves high trial-level accuracy while minimizing calibration time.
neurophysiology, brain-computer interface, visual evoked potential, decoding pipeline
Publication
