Improving Reliability of Life Applications Using Model-based Brain Switches via SSVEP.

The brain switch improves the reliability of asynchronous brain-computer interface (aBCI) systems by switching the control state of the BCI system. Traditional brain switch research focuses on extracting advanced electroencephalography (EEG) features. However, a low signal-to-noise ratio (SNR) of EEG signals resulted in limited feature information and low performance of brain switches.

A model-based brain switch via periodic motor imagery modulation for asynchronous brain-computer interfaces.

Brain switches provide a tangible solution to asynchronized brain-computer interface, which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch.

Channel Selection for Stereo- Electroencephalography (SEEG)-Based Invasive Brain-Computer Interfaces Using Deep Learning Methods.

Brain-computer interfaces (BCIs) can enable direct communication with assistive devices by recording and decoding signals from the brain. To achieve high performance, many electrodes will be used, such as the recently developed invasive BCIs with channel numbers up to hundreds or even thousands. For those high-throughput BCIs, channel selection is important to reduce signal redundancy and invasiveness while maintaining decoding performance.

Data augmentation for invasive brain-computer interfaces based on stereo-electroencephalography (SEEG).

Deep learning is increasingly used for brain-computer interfaces (BCIs). However, the quantity of available data is sparse, especially for invasive BCIs. Data augmentation (DA) methods, such as generative models, can help to address this sparseness.

Spatio-temporal evolution of human neural activity during visually cued hand movements.

Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity.

Electrotactile Feedback Improves Grip Force Control and Enables Object Stiffness Recognition While Using a Myoelectric Hand.

Current myoelectric hands are limited in their ability to provide effective sensory feedback to the users, which highly affects their functionality and utility. Although the sensory information of a myoelectric hand can be acquired with equipped sensors, transforming the sensory signals into effective tactile sensations on users for functional tasks is a largely unsolved challenge. The purpose of this study aims to demonstrate that electrotactile feedback of the grip force improves the sensorimotor control of a myoelectric hand and enables object stiffness recognition.

Phase-amplitude coupling between low-frequency scalp EEG and high-frequency intracranial EEG during working memory task.

. Revealing the relationship between simultaneous scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) is of great importance for both neuroscientific research and translational applications. However, whether prominent iEEG features in the high-gamma band can be reflected by scalp EEG is largely unknown.

Spontaneous State Detection Using Time-Frequency and Time-Domain Features Extracted From Stereo-Electroencephalography Traces.

As a minimally invasive recording technique, stereo-electroencephalography (SEEG) measures intracranial signals directly by inserting depth electrodes shafts into the human brain, and thus can capture neural activities in both cortical layers and subcortical structures. Despite gradually increasing SEEG-based brain-computer interface (BCI) studies, the features utilized were usually confined to the amplitude of the event-related potential (ERP) or band power, and the decoding capabilities of other time-frequency and time-domain features have not been demonstrated for SEEG recordings yet. In this study, we aimed to verify the validity of time-domain and time-frequency features of SEEG, where classification performances served as evaluating indicators.

Highly efficient prime editing by introducing same-sense mutations in pegRNA or stabilizing its structure.

Prime editor (PE), which is developed by combining Cas9 nickase and an engineered reverse transcriptase, can mediate all twelve types of base substitutions and small insertions or deletions in living cells but its efficiency remains low. Here, we develop spegRNA by introducing same-sense mutations at proper positions in the reverse-transcription template of pegRNA to increase PE's base-editing efficiency up-to 4,976-fold (on-average 353-fold). We also develop apegRNA by altering the pegRNA secondary structure to increase PE's indel-editing efficiency up-to 10.

Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings.

Invasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniques typically focus on the signals from the sensorimotor cortex, leaving subcortical regions and other cortical regions related to the movements largely unexplored. As an intracranial recording technique for the presurgical assessments of brain surgery, stereo-encephalography (SEEG) inserts depth electrodes containing multiple contacts into the brain and thus provides the unique opportunity for investigating movement-related neural representation throughout the brain.

Exacerbated AIDS Progression by PD-1 Blockade during Therapeutic Vaccination in Chronically Simian Immunodeficiency Virus-Infected Rhesus Macaques after Interruption of Antiretroviral Therapy.

The persistence of cells latently infected with HIV-1, named the latent reservoir, is the major barrier to HIV-1 eradication, and the formation and maintenance of the latent reservoir might be exacerbated by activation of the immunoinhibitory pathway and dysfunction of CD8 T cells during HIV-1 infection. Our previous findings demonstrated that prophylactic vaccination combined with PD-1 blockade generated distinct immune response profiles and conferred effective control of highly pathogenic SIV infection in rhesus macaques. However, to our surprise, herein we found that a therapeutic vaccination in combination with PD-1 blockade resulted in activation of the viral reservoir, faster viral rebound after treatment interruption, accelerated AIDS progression, and, ultimately, death in chronically SIV-infected macaques after antiretroviral therapy (ART) interruption.

Investigating Data Cleaning Methods to Improve Performance of Brain-Computer Interfaces Based on Stereo-Electroencephalography.

Stereo-electroencephalography (SEEG) utilizes localized and penetrating depth electrodes to directly measure electrophysiological brain activity. The implanted electrodes generally provide a sparse sampling of multiple brain regions, including both cortical and subcortical structures, making the SEEG neural recordings a potential source for the brain-computer interface (BCI) purpose in recent years. For SEEG signals, data cleaning is an essential preprocessing step in removing excessive noises for further analysis.

Detection of human white matter activation and evaluation of its function in movement decoding using stereo-electroencephalography (SEEG).

. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown.

Modulation of Antiviral Immunity and Therapeutic Efficacy by 25-Hydroxycholesterol in Chronically SIV-Infected, ART-Treated Rhesus Macaques.

Cholesterol-25-hydroxylase (CH25H) and its enzymatic product 25-hydroxycholesterol (25HC) exert broadly antiviral activity including inhibiting HIV-1 infection. However, their antiviral immunity and therapeutic efficacy in a nonhuman primate model are unknown. Here, we report that the regimen of 25HC combined with antiretroviral therapy (ART), provides profound immunological modulation towards inhibiting viral replication in chronically SIV-infected rhesus macaques (RMs).

Self-Related Stimuli Decoding With Auditory and Visual Modalities Using Stereo-Electroencephalography.

Name recognition plays important role in self-related cognitive processes and also contributes to a variety of clinical applications, such as autism spectrum disorder diagnosis and consciousness disorder analysis. However, most previous name-related studies usually adopted noninvasive EEG or fMRI recordings, which were limited by low spatial resolution and temporal resolution, respectively, and thus millisecond-level response latencies in precise brain regions could not be measured using these noninvasive recordings. By invasive stereo-electroencephalography (SEEG) recordings that have high resolution in both the spatial and temporal domain, the current study distinguished the neural response to one's own name or a stranger's name, and explored common active brain regions in both auditory and visual modalities.

A brain-actuated robotic arm system using non-invasive hybrid brain-computer interface and shared control strategy.

The electroencephalography (EEG)-based brain-computer interfaces (BCIs) have been used in the control of robotic arms. The performance of non-invasive BCIs may not be satisfactory due to the poor quality of EEG signals, so the shared control strategies were tried as an alternative solution. However, most of the existing shared control methods set the arbitration rules manually, which highly depended on the specific tasks and developer's experience.

Beyond Technologies of Electroencephalography-Based Brain-Computer Interfaces: A Systematic Review From Commercial and Ethical Aspects.

The deployment of electroencephalographic techniques for commercial applications has undergone a rapid growth in recent decades. As they continue to expand in the consumer markets as suitable techniques for monitoring the brain activity, their transformative potential necessitates equally significant ethical inquiries. One of the main questions, which arises then when evaluating these kinds of applications, is whether they should be aligned or not with the main ethical concerns reported by scholars and experts.

Enhancing gesture decoding performance using signals from posterior parietal cortex: a stereo-electroencephalograhy (SEEG) study.

Objective: Hand movement is a crucial function for humans' daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while ignoring the hand movement related signals from posterior parietal cortex (PPC).

iEEGview: an open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes.

Objective: The precise localization of intracranial electrodes is a fundamental step relevant to the analysis of intracranial electroencephalography (iEEG) recordings in various fields. With the increasing development of iEEG studies in human neuroscience, higher requirements have been posed on the localization process, resulting in urgent demand for more integrated, easy-operation and versatile tools for electrode localization and visualization. With the aim of addressing this need, we develop an easy-to-use and multifunction toolbox called iEEGview, which can be used for the localization and visualization of human intracranial electrodes.

Optimal referencing for stereo-electroencephalographic (SEEG) recordings.

Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG.

Human-to-human closed-loop control based on brain-to-brain interface and muscle-to-muscle interface.

Novel communication techniques have always been fascinating for humankind. This pilot study presents an approach to human interaction by combining direct brain-to-brain interface (BBI) and muscle-to-muscle interface (MMI) in a closed-loop pattern. In this system, artificial paths (data flows) functionally connect natural paths (nerves).

Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain.

An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation.