
Eine Gehirn-Computer-Schnittstelle ist ein direkter Kommunikationsweg zwischen Nervengewebe und einem externen Rechensystem, der die herkömmlichen neuromuskulären Ausgabekanäle umgeht, um aufgezeichnete Gehirnaktivität in Gerätebefehle, synthetisierte Sprache oder wiederhergestelltes sensorisches Feedback in Echtzeit zu übersetzen.
Das Feld teilt sich entlang einer Invasivitätsachse: nicht-invasive Modalitäten - Kopfhaut-EEG, funktionelles Nahinfrarot Spektroskopie - bieten Signalerfassung ohne chirurgisches Risiko, aber mit stark eingeschränkter räumlicher Auflösung und Signal-Rausch-Verhältnis; Elektrokortikographie-Gitter, die auf der kortikalen Oberfläche platziert werden, nehmen eine mittlere Stufe ein; und vollständig intrakortikale Ansätze, typisch für das Utah-Array und das flexible Fadenelektrodensystem von Neuralink, zeichnen Einzelspikes von Hunderten bis Tausenden von Neuronen gleichzeitig auf, allerdings auf Kosten der chirurgischen Implantation, der Fremdkörperreaktion und der langfristigen Verschlechterung der Elektrodenstabilität.
Signalverarbeitung Pipelines — Spitze SortierungLokale Feldpotentialzerlegung und zunehmend auf neuronaler Populationsdynamik trainierte Deep-Learning-Decoder übersetzen rohe Elektrophysiologie in hochdimensionale Steuersignale. Motor Cortex-Dekodierung zur Cursorsteuerung und Roboter Gliedmaßenansteuerung und Sprachbereichsdekodierung für die imaginierte oder versuchte Sprachsynthese stellen die beiden klinisch fortschrittlichsten Anwendungsbereiche dar. Geschlossene Schleife Architekturen, die neuronale Aufzeichnungen mit präzise getimter kortikaler oder peripherer Neurostimulation kombinieren, verbessern gleichzeitig die Schlaganfallrehabilitation, die Behandlung therapieresistenter Depressionen und die Epilepsiebehandlung.
Die unten aufgeführten Veröffentlichungen und Patente umfassen die Bereiche Elektrodenmaterialwissenschaft, ASIC-Front-End-Verstärkerdesign, Decoder-Algorithmen, drahtlose neuronale Telemetrie, Biokompatibilitätsstudien und klinische Studie Ergebnisse über das gesamte Invasivitätsspektrum:
Dies ist unsere neueste Auswahl weltweiter englischsprachiger Publikationen und Patente zum Thema Gehirn-Computer-Schnittstellen (BCI). Sie umfasst zahlreiche wissenschaftliche Online-Fachzeitschriften und ist nach folgenden Stichworten klassifiziert: BCI, Gehirn-Computer-Schnittstelle, neuronale Schnittstelle, Elektrokortikographie, intrakortikales Elektrodenarray, Utah-Array, Neuralink-Implantat, Stentroden-BCI, EEG-basierte BCI, Dekodierung des motorischen Kortex, neuronale Spike-Sortierung, lokale Feldpotential-BCI, Spiking-Neural-Network-Decoder, neuronaler Signalverstärker, Closed-Loop-Neurostimulation, BCI-Motorneuroprothese, Sprach-BCI, Dekodierung imaginierter Sprache, BCI-Cursorsteuerung, BCI-Kommunikationsgerät, neuronaler Dekodierungsalgorithmus, BCI-Artefaktunterdrückung, flexible neuronale Sonde, biokompatible neuronale Elektrode und Langzeitstabilität von BCI.
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
Brain-computer interface system and method
Patent published on the 2026-03-26 in WO under Ref WO2026064733 by SCIENCE CORP [US] (Rostov Marat [us], Slager Nate [us], Walker Sage [us], Hodak Max [us], Sharpe Russell [us], Zhou Emma [us], Elsen Antonia [us])
Abstract: Variants of the system can include: a probe and an interface device. Variants of the method can include: configuring a signal pipeline and executing the signal pipeline. In variants, the system and/or method can function to record neural signals from a variety of brain-computer interface (BCI) probe devices. In a specific example, the system and/or method can enable high bandwidth neural recording and processing for BCI experiments.[...]
Our summary: The content describes a brain-computer interface system and method that includes a probe and an interface device. It details the configuration and execution of a signal pipeline to record neural signals from BCI devices. The system aims to enable high bandwidth neural recording and processing for BCI experiments.
brain-computer interface, neural signals, signal pipeline, high bandwidth
Patent
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