Invasive Brain-computer Interface Research Articles

Research advances in non-invasive brain-computer interface control strategies

Brain-computer interface (BCI) can establish a direct communications pathway between the human brain and the external devices, which is independent of peripheral nerves and muscles. Compared with invasive BCI, non-invasive BCI has the advantages of low cost, low risk, and ease of operation. In recent years, using non-invasive BCI technology to control devices has gradually evolved into a new type of human-computer interaction manner. Moreover, the control strategy for BCI is an essential component of this manner. First, this study introduced how the brain control techniques were developed and classified. Second, the basic characteristics of direct and shared control strategies were thoroughly explained. And then the benefits and drawbacks of these two strategies were compared and further analyzed. Finally, the development direction and application prospects for non-invasive brain control strategies were suggested.

Improving the Effectiveness of Learning with the Help of Neurocomputer Interface

The article considers modern technologies for reading signals from the human brain and nervous system and selects the optimal technology to improve the efficiency of adult learning with the help of a neurocomputer interface. Existing brain-computer interfaces (BCI) technologies can be divided into invasive and non-invasive. The first, invasive BCIs, are neuroimplants in certain parts of the brain that work on the basis of electrocorticography (ECOG) or intracranial EEG (iEEG) technology and do not require deep intervention in brain structures; or another invasive BCIs, based on intracortical recording technology using implants with electrodes placed in brain closer to the signal source, and required more complicate operation. The second, non-invasive BCI, reads signals from the brain and nervous system and is based on electroencephalogram (EEG). Compared to invasive BCIs with their more accurate signal, transcranial BCIs communicate with the brain through the skull bones, muscles, and all tissues. Their use does not require intervention in the human body. To increase the effectiveness of training, there was chosen a physiotherapeutic method of transcranial electrical stimulation (TES) in combination with a braincomputer interface based on electroencephalography (EEG), as the most accessible non-invasive method of exposure and feedback due to BCI without known side effects to mental functions and personality. The use of brain-computer interfaces, in particular transcranial electrical stimulation in combination with electroencephalography, increases cognitive abilities in learning, including multitasking. This method can also be used to increase the effectiveness of human assimilation of the necessary new digital environments and is used not only for training complex professions, but also for the masses. Side effects on higher mental functions and personality have not been sufficiently studied to recommend or avoid the use of neurocomputer interfaces for widespread use in education.

Brain-computer interface—Brain information reading and activity control

<p indent="0mm">Brain-computer interface (BCI) is a relatively newly developed technology, which builds up a channel for the communication and interaction between the brain and the outside world. It can be used to gain access to the neural activity information in the brain, decode the intention of the user, and understand higher levels of semantics of the user, as well as used as a means for the regulation and control of brain activities. BCI technology, although experienced only a short period of developments, has already been used in neuroscience for understanding the working principles of the neural systems, in the fields of therapeutic, rehabilitation, and augmentation of neural and physiological functions, as well as exhibited a strong potential for applications in a wide range of fields, such as learning and education, game and entertainment, and automatic control of industrial process and various devices. Since using the technology can not only detect information about the neural activity inside the brain, but also exert stimulation to the nervous system so that the neural activity can be regulated and controlled by designed algorithms, ethical and legal considerations are generated for the research and applications of BCIs. In this paper, the basic principle and structure of BCIs are briefly introduced, and the evolution and present status of the technology are reviewed. Particularly, we present an overview of the development of the technology by introducing some events and techniques in the field, and review the classification of BCIs with respect to the techniques developed for brain signal acquisition, namely, invasive, non-invasive, and semi-invasive BCIs. In information technology, brain signal decoding techniques are critical to BCIs. We review some technical developments particularly for the neural signal decoding algorithms. In terms of the neural activities they deal with, BCIs can also be classified into motor-sensory BCIs and cognitive BCIs. The latter deals with brain activities related to higher order functions, such as memory, language, attention, and decision making, in contrast to the sensory and motor functions. The techniques and systems developed for cognitive BCIs are reviewed. Some challenging problems for the further development of the BCI are described in this paper. The problems are classified and reviewed in three categories. The first category is problems related to performance and applicability of BCIs, including the communication speed, the accuracy of neural signal decoding algorithms, and the applicability problems particularly connected to the biocompatibility issue. The second category is safety and security of BCIs, including safety and reliability of BCI software and hardware, security problems from cyberattacks and use of AI algorithms. The third category is ethical and legal issues facing the design and application of BCIs, including the problems related to privacy, identity and agency, as well as social equality. Some research directions for the further development of BCIs are also presented.

Neurosurgical Team Acceptability of Brain–Computer Interfaces: A Two-Stage International Cross-Sectional Survey

Invasive brain-computer interfaces (BCIs) require neurosurgical implantation, which confers a range of risks. Despite this situation, no studies have assessed the acceptability of invasive BCIs among the neurosurgical team. This study aims to establish baseline knowledge of BCIs within the neurosurgical team and identify attitudes toward different applications of invasive BCI. A 2-stage cross-sectional international survey of the neurosurgical team (neurosurgeons, anesthetists, and operating room nurses) was conducted. Results from the first, qualitative, survey were used to guide the second-stage quantitative survey, which assessed acceptability of invasive BCI applications. Five-part Likert scales were used to collect quantitative data. Surveys were distributed internationally via social media and collaborators. A total of 108 qualitative responses were collected. Themes included the promise of BCIs positively affecting disease targets, concerns regarding stability, and an overall positive emotional reaction to BCI technology. The quantitative survey generated 538 responses from 32 countries. Baseline knowledge of BCI technology was poor, with 9% claiming to have a good or expert knowledge of BCIs. Acceptability of invasive BCI for rehabilitative purposes was >80%. Invasive BCI for augmentation in healthy populations divided opinion. The neurosurgical team's view of the acceptability of invasive BCI was divided across a range of indications. Some applications (e.g., stroke rehabilitation) were viewed as more appropriate than other applications (e.g., augmentation for military use). This range in views highlights the need for stakeholder consultation on acceptable use cases along with regulation and guidance to govern initial BCI implantations if patients are to realize the potential benefits.

Machine learning-based feature combination analysis for odor-dependent hemodynamic responses of rat olfactory bulb

Rodents have a well-developed sense of smell and are used to detect explosives, mines, illegal substances, hidden currency, and contraband, but it is impossible to keep their concentration constantly. Therefore, there is an ongoing effort to infer odors detected by animals without behavioral readings with brain-computer interface (BCI) technology. However, the invasive BCI technique has the disadvantage that long-term studies are limited by the immune response and electrode movement. On the other hand, near-infrared spectroscopy (NIRS)-based BCI technology is a non-invasive method that can measure neuronal activity without worrying about the immune response or electrode movement. This study confirmed that the NIRS-based BCI technology can be used as an odor detection and identification from the rat olfactory system. In addition, we tried to present features optimized for machine learning models by extracting six features, such as slopes, peak, variance, mean, kurtosis, and skewness, from the hemodynamic response, and analyzing the importance of individuals or combinations. As a result, the feature with the highest F1-Score was indicated as slopes, and it was investigated that the combination of the features including slopes and mean was the most important for odor inference. On the other hand, the inclusion of other features with a low correlation with slopes had a positive effect on the odor inference, but most of them resulted in insignificant or rather poor performance. The results presented in this paper are expected to serve as a basis for suggesting the development direction of the hemodynamic response-based bionic nose in the future.

Neuronal Jamming cyberattack over invasive BCIs affecting the resolution of tasks requiring visual capabilities

Invasive Brain-Computer Interfaces (BCIs) are extensively used in medical application scenarios to record, stimulate, or inhibit neural activity with different purposes. An example is the stimulation of some brain areas to reduce the effects generated by Parkinson’s disease. Despite the advances in recent years, cybersecurity on BCIs is an open challenge since attackers can exploit the vulnerabilities of invasive BCIs to induce malicious stimulation or treatment disruption, affecting neuronal activity. In this work, we design and implement a novel neuronal cyberattack called Neuronal Jamming (JAM), which prevents neurons from producing spikes. To implement and measure the JAM impact, and due to the lack of realistic neuronal topologies in mammalians, we have defined a use case using a Convolutional Neural Network (CNN) trained to allow a simulated mouse to exit a particular maze. The resulting model has been translated to a biological neural topology, simulating a portion of a mouse’s visual cortex. The impact of JAM on both biological and artificial networks is measured, analyzing how the attacks can both disrupt the spontaneous neural signaling and the mouse’s capacity to exit the maze. Besides, another contribution of the work focuses on comparing the impacts of both JAM and FLO (an existing neural cyberattack), demonstrating that JAM generates a higher impact in terms of neuronal spike rate. As a final contribution, we discuss whether and how JAM and FLO attacks could induce the effects of neurodegenerative diseases if the implanted BCI had a comprehensive electrode coverage of the targeted brain regions.

Introduction of brain computer interface to neurologists

A brain-computer interface (BCI) is a technology that acquires and analyzes electrical signals from the brain to control external devices. BCI technologies can generally be used to control a computer cursor, limb orthosis, or word processing. This technology can also be used as a neurological rehabilitation tool for people with poor motor control. We reviewed historical attempts and methods toward predicting arm movements using brain waves. In addition, representative studies of minimally invasive and noninvasive BCI were summarized.

Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand.

Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore impairments in patients living with these debilitating conditions. One of the challenges currently facing BCI technology, however, is to minimize surgical risk while maintaining efficacy. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications in epilepsy patients since they can lead to fewer complications. SEEG depth electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. Here we show the first demonstration of decoding sulcal and subcortical activity related to both movement and tactile sensation in the human hand. Furthermore, we have compared decoding performance in SEEG-based depth recordings versus those obtained with electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns varied in amplitude trial-to-trial and were transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on a repeatability metric using temporal correlation. An algorithm based on temporal correlation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling transient input features. Combining temporal correlation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 ± 1.51% for hand movements, up to 91.69 ± 0.49% for individual finger movements, and up to 83.49 ± 0.72% for focal tactile stimuli to individual finger pads while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wide variety of conditions.

Decoding Saccade Intention From Primate Prefrontal Cortical Local Field Potentials Using Spectral, Spatial, and Temporal Dimensionality Reduction.

Most invasive Brain Computer Interfaces (iBCIs) use spike and Local Field Potentials (LFPs) from the motor or parietal cortices to decode movement intentions. It has been debated whether harvesting signals from other brain areas that encode global cognitive variables, such as the allocation of attention and eye movement goals in a variety of spatial reference frames, may improve the outcome of iBCIs. Here, we explore the ability of LFP signals, sampled from the lateral prefrontal cortex (LPFC) of macaque monkeys, to encode eye-movement intention during the pre-movement fixation period of a delayed saccade task. We use spectral dimensionality reduction to examine the spatiotemporal properties of the extracted non-rhythmic broadband activity and explore its usefulness in decoding saccade goals. The dynamics of the broadband signal in low spatial dimensions across the pre-movement fixation period uncovered saccade target separation; its discriminative potential was confirmed using support vector machine classifications. These findings reveal that broadband LFP from the LPFC can be used to decode intended saccade target location during pre-movement periods. We further provide a general workflow that can be implemented in iBCIs and it is relatively robust to the loss of spikes in individual electrodes.

Invasive Brain Computer Interface Systems and the Right to Human Flourishing: Extent of Sufficiency of Article 8 of the European Convention of Human Rights to Regulate Invasive Brain Computer Interface Systems to Ensure the Right to Human Flourishing

Invasive Brain Computer Interface Systems and the Right to Human Flourishing: Extent of Sufficiency of Article 8 of the European Convention of Human Rights to Regulate Invasive Brain Computer Interface Systems to Ensure the Right to Human Flourishing

Progress in the brain-computer interface: an interview with Bin He.

What can the brain–computer interface (BCI) do? Wearing an electroencephalogram (EEG) headcap, you can control the flight of a drone in the laboratory by your thought; with electrodes inserted inside the brain, paralytic patients can drink by controlling a robotic arm with thinking. Both invasive and non-invasive BCI try to connect human brains to machines. In the past several decades, BCI technology has continued to develop, making science fiction into reality and laboratory inventions into indispensable gadgets. In July 2019, Neuralink, a company founded by Elon Musk, proposed a sewing machine-like device that can dig holes in the skull and implant 3072 electrodes onto the cortex, promising more accurate reading of what you are thinking, although many serious scientists consider the claim misleading to the public. Recently, National Science Review (NSR) interviewed Professor Bin He, the department head of Biomedical Engineering at Carnegie Mellon University, and a leading scientist in the non-invasive-BCI field. His team developed new methods for non-invasive BCI to control drones by thoughts. In 2019, Bin’s team demonstrated the control of a robotic arm to follow a continuously randomly moving target on the screen. In this interview, Bin He recounted the history of BCI, as well as the opportunities and challenges of non-invasive BCI.

A Therapy for Nightmares through Invasive Brain Computer Interface (BCI)

Brain Computer Interfacing (BCI) is progression which establishes a correlation or a trail used intended for statement from outside humankind to the mind all the way through the brain signals. There are three popular types of BCI, based on top of the placement of sensors worn for tracing aforesaid signals, which are non-invasive, invasive semi-invasive and invasive. Non-invasive BCI is one where sensors are located on top of the scalp, computing also the electrical potentials shaped with mind that is defined as elec-troencephalography (EEG) or the magnetic fields by means of a method referred Magneto encephalography (MEG); in partial-invasive BCI electrodes be positioned on top of the bare exterior of the brain in practice referred as Electrocorticography (ECoG); in addition to invasive, micro-electrode arrays be positioned in a straight line to cortex. This paper proposes a scheme for invasive BCI process for treatment of nightmares.

Feasibility of identifying the ideal locations for motor intention decoding using unimodal and multimodal classification at 7T-fMRI

Invasive Brain-Computer Interfaces (BCIs) require surgeries with high health-risks. The risk-to-benefit ratio of the procedure could potentially be improved by pre-surgically identifying the ideal locations for mental strategy classification. We recorded high-spatiotemporal resolution blood-oxygenation-level-dependent (BOLD) signals using functional MRI at 7 Tesla in eleven healthy participants during two motor imagery tasks. BCI diagnostic task isolated the intent to imagine movements, while BCI simulation task simulated the neural states that may be yielded in a real-life BCI-operation scenario. Imagination of movements were classified from the BOLD signals in sub-regions of activation within a single or multiple dorsal motor network regions. Then, the participant’s decoding performance during the BCI simulation task was predicted from the BCI diagnostic task. The results revealed that drawing information from multiple regions compared to a single region increased the classification accuracy of imagined movements. Importantly, systematic unimodal and multimodal classification revealed the ideal combination of regions that yielded the best classification accuracy at the individual-level. Lastly, a given participant’s decoding performance achieved during the BCI simulation task could be predicted from the BCI diagnostic task. These results show the feasibility of 7T-fMRI with unimodal and multimodal classification being utilized for identifying ideal sites for mental strategy classification.

A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces

Recent advancements in brain computer interfaces (BCI) have demonstrated control of robotic systems by mental processes alone. Together with invasive BCI, electroencephalographic (EEG) BCI represent an important direction in the development of BCI systems. In the context of EEG BCI, the processing of EEG data is the key challenge. Unfortunately, advances in that direction have been complicated by a lack of large and uniform datasets that could be used to design and evaluate different data processing approaches. In this work, we release a large set of EEG BCI data collected during the development of a slow cortical potentials-based EEG BCI. The dataset contains 60 h of EEG recordings, 13 participants, 75 recording sessions, 201 individual EEG BCI interaction session-segments, and over 60 000 examples of motor imageries in 4 interaction paradigms. The current dataset presents one of the largest EEG BCI datasets publically available to date.

Decoding of intended saccade direction in an oculomotor brain–computer interface

Objective. To date, invasive brain–computer interface (BCI) research has largely focused on replacing lost limb functions using signals from the hand/arm areas of motor cortex. However, the oculomotor system may be better suited to BCI applications involving rapid serial selection from spatial targets, such as choosing from a set of possible words displayed on a computer screen in an augmentative and alternative communication (AAC) application. Here we aimed to demonstrate the feasibility of a BCI utilizing the oculomotor system. Approach. We developed a chronic intracortical BCI in monkeys to decode intended saccadic eye movement direction using activity from multiple frontal cortical areas. Main results. Intended saccade direction could be decoded in real time with high accuracy, particularly at contralateral locations. Accurate decoding was evident even at the beginning of the BCI session; no extensive BCI experience was necessary. High-frequency (80–500 Hz) local field potential magnitude provided the best performance, even over spiking activity, thus simplifying future BCI applications. Most of the information came from the frontal and supplementary eye fields, with relatively little contribution from dorsolateral prefrontal cortex. Significance. Our results support the feasibility of high-accuracy intracortical oculomotor BCIs that require little or no practice to operate and may be ideally suited for ‘point and click’ computer operation as used in most current AAC systems.

Simulation of a Real-Time Brain Computer Interface for Detecting a Self-Paced Hitting Task

An invasive brain-computer interface (BCI) is a promising neurorehabilitation device for severely disabled patients. Although some systems have been shown to work well in restricted laboratory settings, their utility must be tested in less controlled, real-time environments. Our objective was to investigate whether a specific motor task could be reliably detected from multiunit intracortical signals from freely moving animals in a simulated, real-time setting. Intracortical signals were first obtained from electrodes placed in the primary motor cortex of four rats that were trained to hit a retractable paddle (defined as a "Hit"). In the simulated real-time setting, the signal-to-noise-ratio was first increased by wavelet denoising. Action potentials were detected, and features were extracted (spike count, mean absolute values, entropy, and combination of these features) within pre-defined time windows (200 ms, 300 ms, and 400 ms) to classify the occurrence of a "Hit." We found higher detection accuracy of a "Hit" (73.1%, 73.4%, and 67.9% for the three window sizes, respectively) when the decision was made based on a combination of features rather than on a single feature. However, the duration of the window length was not statistically significant (p = 0.5). Our results showed the feasibility of detecting a motor task in real time in a less restricted environment compared to environments commonly applied within invasive BCI research, and they showed the feasibility of using information extracted from multiunit recordings, thereby avoiding the time-consuming and complex task of extracting and sorting single units.

BRAIN-COMPUTER INTERFACE FOR THE AUGMENTATION OF BRAIN FUNCTIONS

Brain-computer interface (BCI) connects the nervous system departments with external devices for the purpose of recovery of motor and sensory functions of patients with neurological lesions. Over the past half-century BCI have gone from initial ideas to the high-tech modern incarnations. This development contributed significantly to the invasive techniques of multichannel registration activity of neuronal ensembles. Modern BCI are able to manage mechanical prosthetic arms and legs. Furthermore, BCI can provide sensory feedback, allowing the user to feel the movement of the prosthesis and its interaction with external objects. Latest BCI connect multiple users to the brain network. In this review, these achievements are dealt with a focus on invasive BCI.

On Similarities and Differences of Invasive and Non-Invasive Electrical Brain Signals in Brain-Computer Interfacing

We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although invasive and non-invasive BCI signals are different, the underlying origin of electrical BCIs signals is the same.

Artificial feedback for invasive brain–computer interfaces

During the last two decades, considerable progress has been made in the studies of brain-computer interfaces (BCIs)--devices in which motor signals from the brain are registered by multi-electrode arrays and transformed into commands for articial actuators such as cursors and robotic devices. This review is focused on one problem. Voluntary motor control is based on neurophysiological processes which depend heavily on the afferent innervation of skin, muscles and joints. Thus, invasive BCI has to be based on a bidirectional system in which motor control signals are registered by multi-channel micro-electrodes implanted in motor areas, while tactile, proprioceptive and other useful signals are transported back to the brain through spatial-temporal patterns of intracortical microstimulation (ICMS) delivered to sensory areas. In general, the studies of invasive BCIs have advanced in several directions. The progress of BCIs with articial sensory feedback will not only help patients, but will also expand knowledge base in the field of human cortical functions.

The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia.

BackgroundDirect brain control of overground walking in those with paraplegia due to spinal cord injury (SCI) has not been achieved. Invasive brain-computer interfaces (BCIs) may provide a permanent solution to this problem by directly linking the brain to lower extremity prostheses. To justify the pursuit of such invasive systems, the feasibility of BCI controlled overground walking should first be established in a noninvasive manner. To accomplish this goal, we developed an electroencephalogram (EEG)-based BCI to control a functional electrical stimulation (FES) system for overground walking and assessed its performance in an individual with paraplegia due to SCI.MethodsAn individual with SCI (T6 AIS B) was recruited for the study and was trained to operate an EEG-based BCI system using an attempted walking/idling control strategy. He also underwent muscle reconditioning to facilitate standing and overground walking with a commercial FES system. Subsequently, the BCI and FES systems were integrated and the participant engaged in several real-time walking tests using the BCI-FES system. This was done in both a suspended, off-the-ground condition, and an overground walking condition. BCI states, gyroscope, laser distance meter, and video recording data were used to assess the BCI performance.ResultsDuring the course of 19 weeks, the participant performed 30 real-time, BCI-FES controlled overground walking tests, and demonstrated the ability to purposefully operate the BCI-FES system by following verbal cues. Based on the comparison between the ground truth and decoded BCI states, he achieved information transfer rates >3 bit/s and correlations >0.9. No adverse events directly related to the study were observed.ConclusionThis proof-of-concept study demonstrates for the first time that restoring brain-controlled overground walking after paraplegia due to SCI is feasible. Further studies are warranted to establish the generalizability of these results in a population of individuals with paraplegia due to SCI. If this noninvasive system is successfully tested in population studies, the pursuit of permanent, invasive BCI walking prostheses may be justified. In addition, a simplified version of the current system may be explored as a noninvasive neurorehabilitative therapy in those with incomplete motor SCI.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-015-0068-7) contains supplementary material, which is available to authorized users.