MétaCan
Menu
Back to cohort
Record W4402894358 · doi:10.3389/fnins.2024.1485472

Editorial: New approaches in Brain-Machine Interfaces with implants

2024· editorial· en· W4402894358 on OpenAlexaff
Vahid Salari, Rodney P. O’Connor, Serafim Rodrigues, Daniel Oblak

Bibliographic record

VenueFrontiers in Neuroscience · 2024
Typeeditorial
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsPolytechnique MontréalUniversity of Calgary
FundersEuropean Regional Development FundMinisterio de Ciencia e InnovaciónAgencia Estatal de InvestigaciónBasque Center for Applied MathematicsEusko Jaurlaritza
KeywordsBrain implantComputer scienceBrain–computer interfaceNeuroscienceHuman–computer interactionArtificial intelligencePsychologyElectroencephalography

Abstract

fetched live from OpenAlex

Brain-Machine Interfaces (BMIs) or Brain-Computer Interfaces (BCIs) open new possibilities in neuroscience, creating direct interaction between the brain and external devices \cite{0}. These systems can help individuals with disabilities by translating brain signals into commands. Despite recent progress in BCI with implants, the field still encounters many technological and biological obstacles. For instance, they are working on developing a much smaller, more powerful implant that can be placed in the brain after a simple surgery which could bring control to people with paralysis. However, it is anticipated that progress in the field of brain implants has been hampered by a combination of technological and biological factors, such as the limited understanding of the long-term behaviour of implants, unreliability of devices, and biocompatibility of the implants among others. Invasive BCI requires surgery to implant electrodes under the scalp for communicating brain signals. The main advantage is to provide a more accurate reading; however, its downside includes side effects from the surgery. After the surgery, scar tissues may form which can make brain signals weaker.This special issue of Frontiers in Neuroscience investigates new findings related to some challenges in the field and advancing BCI capabilities.\subsubsection{Printable Devices for Neurotechnologies}The first article reviews the growing field of printable electronics for neurotechnology \cite{1}, which proposes rapid prototyping, scalability, and cost-effectiveness. The study features the development of printable neuro-probes and microelectrode arrays for recording and stimulating neuronal activity. These devices, made from biocompatible and flexible materials, aim to enhance our understanding of brain functions and improve treatments for neurological disorders. The potential of printable electronics to facilitate more effective communication between the brain and external systems sets the stage for the transformative advancements discussed in this issue. Printable chips for neurotechnologies include microelectrode arrays (MEAs) that record and stimulate neuronal activity for high-resolution monitoring of brain signals. Electrocorticography (ECoG) arrays enable the recording of electrical activity from the brain's surface, useful for both research and clinical applications. Additionally, 3D-printed neural probes facilitate minimally invasive insertion into brain tissue for targeted stimulation and recording of neural circuits. Bioelectronic interfaces integrate printed electrodes with biological systems for applications like drug delivery and real-time physiological monitoring, while flexible sensors conform to the brain's surface, providing a comfortable interface for long-term monitoring and interaction with neural tissues.\subsubsection{Photonic Neural Probes with 3D Printed Microfluidics}Building on the theme of new materials and methods, the second paper focuses on photonic neural probes integrated with 3D-printed microfluidics \cite{2}. These probes combine optogenetic stimulation and neurochemical delivery, proposing precise control over neural circuits and real-time monitoring of neurochemical changes. The study shows successful neurochemical injections and localized photoactivation in brain tissue, showcasing the potential of these advanced probes to develop therapeutic strategies and improve BCI performance.The research focuses on the development and optimization of neural probes for enhanced neurochemical delivery and monitoring. Key findings include the variation in transmission of grating emitters, which ranged from −27 to −20 dB, with a median of −22 dB due to alignment drift. Compensation techniques such as laser power modulation and MEMS mirror adjustments were employed to address these issues. Additionally, the integration of 3D-printed microfluidic structures allows for neurochemical injection without altering existing fabrication processes, leading to successful uncaging experiments in fixed brain tissue that demonstrated localized photoactivation of caged dyes. The study investigates the potential of these advanced neural probes in neuroscience research for more precise and effective methods of studying neural activity and drug delivery.\subsubsection{ECoG Devices: Assessing Long-Term Biocompatibility}The third study investigates the long-term biocompatibility of Parylene HT -ITO ECoG devices through immunohistochemical evaluations in mice \cite{3}. The research discloses an initial astroglial response that diminishes over time and a transient reduction in cortical thickness, which normalizes in later assessments. Neuronal density was lower on the implanted side only at the last evaluation, suggesting a potential stabilization of neuronal health, while cortical thickness was reduced in the initial assessments but returned to normal by the last time point, showing a recovery of cortical structure. The research discusses the suitability of Parylene HT / ITO ECoG devices for chronic applications, focusing on the importance of evaluating biocompatibility to ensure the safe and effective use of implantable devices in BCI applications. The result shows the long-term biocompatibility of the Parylene HT / ITO ECoG devices, suggesting their feasibility for chronic use in neuroscience applications. \subsubsection{Understanding LFP Polarity for Enhanced BCI Performance}The fourth article investigates local field potentials (LFPs) and current source density (CSD) in the primary visual cortex (V1) of macaque monkeys \cite{4}. By analyzing how stimulus size and eccentricity influence neural activity across cortical layers, the study provides an understanding of the neural mechanisms underlying visual processing. Improved understanding of LFPs and CSD can enhance the accuracy and effectiveness of BCIs, aiding individuals with disabilities in controlling devices and facilitating communication.\subsubsection{The World of Neurohackers: Ethical and Practical Implications}The final paper explores the practices and perspectives of neurohackers, individuals who engage with neurotechnology for personal enhancement and development \cite{5}. Through qualitative interviews, the study reveals diverse motivations and ethical concerns associated with neurohacking. Examples such as RFID chip implants for personal use and BCIs for device control illustrate the innovative and sometimes controversial nature of neurohacking. The research investigates the need for a deeper understanding of the ethical, safety, and societal implications as this field continues to evolve.% \subsubsection{Conclusion}The research presented in this special issue of Frontiers in Neuroscience features the dynamic and multifaceted nature of BCI technologies. From the rapid prototyping capabilities of printable electronics to the precision of photonic neural probes and the ethical considerations of neurohacking, these studies focus on considerable developments toward overcoming current limitations. As we continue to explore and transform, these advancements suggest paving the way for more effective, minimally invasive, and adaptable BCIs, ultimately enhancing the quality of life for individuals with neurological conditions and disabilities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.262
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in NeuroscienceSame topicNeuroscience and Neural EngineeringFrench-language works237,207