Focal cooling: An alternative treatment for drug-resistant epilepsy in a mesial temporal lobe epilepsy primate model-A preliminary study.

Objective: Focal cooling is emerging as a relevant therapy for drug-resistant epilepsy (DRE). However, we lack data on its effectiveness in controlling seizures that originate in deep-seated areas like the hippocampus. We present a thermoelectric solution for focal brain cooling that specifically targets these brain structures.

Walking naturally after spinal cord injury using a brain-spine interface.

A spinal cord injury interrupts the communication between the brain and the region of the spinal cord that produces walking, leading to paralysis. Here, we restored this communication with a digital bridge between the brain and spinal cord that enabled an individual with chronic tetraplegia to stand and walk naturally in community settings. This brain-spine interface (BSI) consists of fully implanted recording and stimulation systems that establish a direct link between cortical signals and the analogue modulation of epidural electrical stimulation targeting the spinal cord regions involved in the production of walking.

Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance.

Introduction: In brain-computer interfaces (BCI) research, recording data is time-consuming and expensive, which limits access to big datasets. This may influence the BCI system performance as machine learning methods depend strongly on the training dataset size. Important questions arise: taking into account neuronal signal characteristics (e.

Online adaptive group-wise sparse Penalized Recursive Exponentially Weighted N-way Partial Least Square for epidural intracranial BCI.

Introduction: Motor Brain-Computer Interfaces (BCIs) create new communication pathways between the brain and external effectors for patients with severe motor impairments. Control of complex effectors such as robotic arms or exoskeletons is generally based on the real-time decoding of high-resolution neural signals. However, high-dimensional and noisy brain signals pose challenges, such as limitations in the generalization ability of the decoding model and increased computational demands.

Unsupervised adaptation of an ECoG based brain-computer interface using neural correlates of task performance.

Brain-computer interfaces (BCIs) translate brain signals into commands to external effectors, and mainly target severely disabled users. The usability of BCIs may be improved by reducing their major constraints, such as the necessity for special training sessions to initially calibrate and later keep up to date the neural signal decoders. In this study, we show that it is possible to train and update BCI decoders during free use of motor BCIs.

Decoding ECoG signal into 3D hand translation using deep learning.

Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. BCIs would potentially compensate for arm and hand function loss, which is the top priority for individuals with tetraplegia. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients in a real-life environment.

An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic.

The article aims at addressing 2 challenges to step motor brain-computer interface (BCI) out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration.Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time.

Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.

The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI.

Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients.

The evaluation of the long-term stability of ElectroCorticoGram (ECoG) signals is an important scientific question as new implantable recording devices can be used for medical purposes such as Brain-Computer Interface (BCI) or brain monitoring.The long-term clinical validation of wireless implantable multi-channel acquisition system for generic interface with neurons (WIMAGINE), a wireless 64-channel epidural ECoG recorder was investigated. The WIMAGINE device was implanted in two quadriplegic patients within the context of a BCI protocol.

Space-Time-Frequency Multi-Sensor Analysis for Motor Cortex Localization Using Magnetoencephalography.

Brain source imaging and time frequency mapping (TFM) are commonly used in magneto/electro encephalography (M/EEG) imaging. However, these methods suffer from important limitations. Source imaging is based on an ill-posed inverse problem leading to instability of source localization solutions, has a limited capacity to localize high frequency oscillations and loses its robustness for induced responses (ill-defined trigger).

An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: a proof-of-concept demonstration.

Background: Approximately 20% of traumatic cervical spinal cord injuries result in tetraplegia. Neuroprosthetics are being developed to manage this condition and thus improve the lives of patients. We aimed to test the feasibility of a semi-invasive technique that uses brain signals to drive an exoskeleton.

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review.

Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses.

Recursive Exponentially Weighted N-way Partial Least Squares Regression with Recursive-Validation of Hyper-Parameters in Brain-Computer Interface Applications.

A tensor-input/tensor-output Recursive Exponentially Weighted N-Way Partial Least Squares (REW-NPLS) regression algorithm is proposed for high dimension multi-way (tensor) data treatment and adaptive modeling of complex processes in real-time. The method unites fast and efficient calculation schemes of the Recursive Exponentially Weighted PLS with the robustness of tensor-based approaches. Moreover, contrary to other multi-way recursive algorithms, no loss of information occurs in the REW-NPLS.

A Long-Term BCI Study With ECoG Recordings in Freely Moving Rats.

Background: Brain Computer Interface (BCI) studies are performed in an increasing number of applications. Questions are raised about electrodes, data processing and effectors. Experiments are needed to solve these issues.

Penalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording.

In the current paper the decoding algorithms for motor-related BCI systems for continuous upper limb trajectory prediction are considered. Two methods for the smooth prediction, namely Sobolev and Polynomial Penalized Multi-Way Partial Least Squares (PLS) regressions, are proposed. The methods are compared to the Multi-Way Partial Least Squares and Kalman Filter approaches.

CLINATEC® BCI platform based on the ECoG-recording implant WIMAGINE® and the innovative signal-processing: preclinical results.

The goal of the CLINATEC® Brain Computer Interface (BCI) Project is to improve tetraplegic subjects' quality of life by allowing them to interact with their environment through the control of effectors, such as an exoskeleton. The BCI platform is based on a wireless 64-channel ElectroCorticoGram (ECoG) recording implant WIMAGINE®, designed for long-term clinical application, and a BCI software environment associated to a 4-limb exoskeleton EMY (Enhancing MobilitY). Innovative ECoG signal decoding algorithms will allow the control of the exoskeleton by the subject's brain activity.

Recursive N-way partial least squares for brain-computer interface.

In the article tensor-input/tensor-output blockwise Recursive N-way Partial Least Squares (RNPLS) regression is considered. It combines the multi-way tensors decomposition with a consecutive calculation scheme and allows blockwise treatment of tensor data arrays with huge dimensions, as well as the adaptive modeling of time-dependent processes with tensor variables. In the article the numerical study of the algorithm is undertaken.

L1-penalized N-way PLS for subset of electrodes selection in BCI experiments.

Recently, the N-way partial least squares (NPLS) approach was reported as an effective tool for neuronal signal decoding and brain-computer interface (BCI) system calibration. This method simultaneously analyzes data in several domains. It combines the projection of a data tensor to a low dimensional space with linear regression.

Deep brain stimulation: BCI at large, where are we going to?

Unlabelled: Brain-computer interfaces (BCIs) include stimulators, infusion devices, and neuroprostheses. They all belong to functional neurosurgery. Deep brain stimulators (DBS) are widely used for therapy and are in need of innovative evolutions.