Brain Switch Research Articles

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

Objective. 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. Approach. Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model. Main results. Our motor imagery (MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58 s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/h was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6 s. Significance. This study provides a new approach that could significantly reduce the brain switch’s FPR to less than 1 Fps/h, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.

Towards Implantable Brain-Computer Interface for Communication in Locked-In Syndrome patients

Abstract Locked-in Syndrome (LIS) severely restricts the communication abilities of individuals due to extensive paralysis. The Intracranial Neuro Telemetry to Restore communication (INTRECOM) project aims to aid patients in overcoming these limitations by developing a fully implantable brain computer interface (BCI) system based on state-of-the-art technology holding great promise in revolutionizing the lives of LIS patients. In this project, the Graz BCI group, with its expertise in understanding brain dynamics associated with movement, focuses on the algorithmic development of advanced decoders that enable the user to execute specific commands by simply attempting corresponding movements. Our preliminary findings using electrocorticography (ECoG) data obtained from one individual with LIS that underwent implantation of a BCI communication system demonstrate the applicability of a ‘brain switch’ function that detects brain signals associated with attempted movements. This switch can then be used to translate the intention of the user into a click/select function on a screen.

A high-performance brain switch based on code-modulated visual evoked potentials

Objective. Asynchronous brain-computer interfaces (BCIs) are more practical and natural compared to synchronous BCIs. A brain switch is a standard asynchronous BCI, which can automatically detect the specified change of the brain and discriminate between the control state and the idle state. The current brain switches still face challenges on relatively long reaction time (RT) and high false positive rate (FPR). Approach. In this paper, an online electroencephalography-based brain switch is designed to realize a fast reaction and keep long idle time (IDLE) without false positives (FPs) using code-modulated visual evoked potentials (c-VEPs). Two stimulation paradigms were designed and compared in the experiments: multi-code concatenate modulation (concatenation mode) and single-code periodic modulation (periodic mode). Using a task-related component analysis-based detection algorithm, EEG data can be decoded into a series of code indices. Brain states can be detected by a template matching approach with a sliding window on the output series. Main results. The online experiments achieved an average RT of 1.49 s when the average IDLE for each FP was 68.57 min (1.46 × 10−2 FP min−1) or an average RT of 1.67 s without FPs. Significance. This study provides a practical c-VEP based brain switch system with both fast reaction and low FPR during idle state, which can be used in various BCI applications.

A morphospace of functional configuration to assess configural breadth based on brain functional networks.

The quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.

Intracranial brain stimulation modulates fMRI-based network switching

The extent to which functional MRI (fMRI) reflects direct neuronal changes remains unknown. Using 160 simultaneous electrical stimulation (es-fMRI) and intracranial brain stimulation recordings acquired in 26 individuals with epilepsy (with varying electrode locations), we tested whether brain networks dynamically change during intracranial brain stimulation, aiming to establish whether switching between brain networks is reduced after intracranial brain stimulation. As the brain spontaneously switches between a repertoire of intrinsic functional network configurations and the rate of switching is likely increased in epilepsy, we hypothesised that intracranial stimulation would reduce the brain's switching rate, thus potentially normalising aberrant brain network dynamics. To test this hypothesis, we quantified the rate that brain regions changed networks over time in response to brain stimulation, using network switching applied to multilayer modularity analysis of time-resolved es-fMRI connectivity. Network switching and synchrony was decreased after the first brain stimulation, followed by a more consistent pattern of network switching over time. This change was commonly observed in cortical networks and adjacent to the electrode targets. Our results suggest that neuronal perturbation is likely to modulate large-scale brain networks, and multilayer network modelling may be used to inform the clinical efficacy of brain stimulation in epilepsy.

Parenting brain switch, vanishing journals and COVID-19 in kids

Parenting brain switch, vanishing journals and COVID-19 in kids

Enhanced Performance of a Brain Switch by Simultaneous Use of EEG and NIRS Data for Asynchronous Brain-Computer Interface.

Previous studies have shown the superior performance of hybrid electroencephalography (EEG)/ near-infrared spectroscopy (NIRS) brain-computer interfaces (BCIs). However, it has been veiled whether the use of a hybrid EEG/NIRS modality can provide better performance for a brain switch that can detect the onset of the intention to turn on a BCI. In this study, we developed such a hybrid EEG/NIRS brain switch and compared its performance with single modality EEG- and NIRS-based brain switch respectively, in terms of true positive rate (TPR), false positive rate (FPR), onset detection time (ODT), and information transfer rate (ITR). In an offline analysis, the performance of a hybrid EEG/NIRS brain switch was significantly improved over that of EEG- and NIRS-based brain switches in general, and in particular a significantly lower FPR was observed for the hybrid EEG/NIRS brain switch. A pseudo-online analysis was additionally performed to confirm the feasibility of implementing an online BCI system with our hybrid EEG/NIRS brain switch. The overall trend of pseudo-online analysis results generally coincided with that of the offline analysis results. No significant difference in all performance measures was also found between offline and pseudo online analysis schemes when the amount of training data was same, with one exception for the ITRs of an EEG brain switch. These offline and pseudo-online results demonstrate that a hybrid EEG/NIRS brain switch can be used to provide a better onset detection performance than that of a single neuroimaging modality.

Brain switch controls glow-worm light

What look like mesmerising sparkles on the roof of dank Australian rainforest caves spell almost certain doom for the insects and other creepy-crawlies lured in by the fatal illumination. David Merritt, from The University of Queensland, Australia, explains that each pinprick of light is produced by an Arachnocampa flava larva, which glimmers continually from dusk to dawn. Adding that the spectacle also lures in human tourists, Merritt explains that the light show is produced by a modified portion of the Malpighian tubule – the insect kidney – and it was thought that the brake that inactivates the light by day is itself dramatically deactivated at night by the anaesthetic effects of CO2. In addition, Merritt knew that the glow-worms turn up the brightness when they sense the vibrations produced by torrential rain, yet he wasn't so sure exactly how these two unrelated phenomena trigger the larvae to turn up their brightness, so he and student Hamish Charlton began investigating.The pair drove to Springbrook National Park at dusk to collect the glowing larvae from the steep earthen banks beside a waterfall, and then provided each animal with its own personal mud-lined mini-cave to keep them comfortable back in Brisbane. Then they increased the CO2 supply to the insects and watched; ‘The light brightened, casting a blue-green glow’, describes Merritt. However, when the duo tested the effects of various anaesthetics, including ethyl acetate and diethyl ether, on the larvae – to see whether these would also raise a glimmer – they found none, ruling out the possibility that CO2 also works by anaesthetising the brake, allowing the insects to put on their light show.In a bid to narrow down exactly whether the brain or the glowing light organ responds directly to CO2 to flip on the light switch, Merritt and Charlton supplied both organs individually with the gas and waited for the insects to begin to glimmer. ‘The larvae are only 1–2 cm long and the light organ is only 1 mm or so in diameter, so dissecting the larvae and removing the light organ without damaging the cells was a challenge’, admits Merritt. However, when the duo bathed the brains of larvae in CO2, they were not able to trigger light production, which Merritt says casts doubt on the models suggesting that signals from the brain repress bioluminescence.In contrast, bathing the light organ in CO2 did trigger a glow, showing that the gas triggers light production directly in the light organ. Also, when the team tested the effects of rainfall on the insects’ ability to produce light, giving them a shake ranging from 180 to 200 Hz, the insects slowly turned up the glow to dazzling levels, leading Merritt to conclude that the increase in light is signalled from the brain.Taking all of the observations into account, Merritt suspects that instead of switching off suppression of the larvae's glow to allow the brilliant insects to shine at night, the insects’ nervous system actively activates the light show by delicately regulating the balance of CO2 and oxygen – which fuels the production of light – in the cells that produce the enigmatic glow. Although accessing the insects to learn more about their ethereal light show became more challenging when the parks closed during the initial stages of the 2020 coronavirus pandemic, they have now reopened, allowing scientists and tourists alike to return and delight in the bewitching experience.

Brain switch offers clues to suspended animation

Brain switch offers clues to suspended animation

Continuous 2D control via state-machine triggered by endogenous sensory discrimination and a fast brain switch

Objective. Brain computer interfacing (BCI) is a promising method to control assistive systems for patients with severe disabilities. Recently, we have presented a novel BCI approach that combines an electrotactile menu and a brain switch, which allows the user to trigger many commands robustly and efficiently. However, the commands are timed to periodic tactile cues and this may challenge online control. In the present study, therefore, we implemented and evaluated a novel approach for online closed-loop control using the proposed BCI. Approach. Eleven healthy subjects used the novel method to move a cursor in a 2D space. To assure robust control with properly timed commands, the BCI was integrated within a state machine allowing the subject to start the cursor movement in the selected direction and asynchronously stop the cursor. The brain switch was controlled using motor execution (ME) or imagery (MI) and the menu implemented four (straight movements) or eight commands (straight and diagonal movements). Main results. The results showed a high completion rate of a target hitting task (~97% and ~92% for ME and MI, respectively), with a small number of collisions, when four-channel control was used. There was no significant difference in outcome measures between MI and ME, and performance was similar for four and eight commands. Significance. These results demonstrate that the novel state-based scheme driven by a robust BCI can be successfully utilized for online control. Therefore, it can be an attractive solution for providing the user an online-control interface with many commands, which is difficult to achieve using classic BCI solutions.

A Fast Brain Switch Based on Multi-Class Code-Modulated VEPs.

To realize asynchronous control of a brain-computer interface (BCI) system, a fast brain switch with low false positive rate (FPR) is required. This paper proposed a brain switch based on code-modulated visual-evoked potential (c-VEP), in which seven 8-bit pseudorandom codes were used to modulate the electroencephalogram (EEG) signal. This study optimized and demonstrated the control strategy through an offline and an online experiments. By decoding the brain state continuously with the task-related component analysis (TRCA) algorithm, the brain switch achieved an average reaction time (RT) of 1.72 seconds and an average idle time of 183.53 seconds without false positive events in the online experiment.

An Asynchronous Hybrid Spelling Approach Based on EEG-EOG Signals for Chinese Character Input.

In this paper, we presented a novel asynchronous speller for Chinese sinogram input by incorporating electroencephalography (EOG) into the conventional electroencephalography (EEG)-based spelling paradigm. An EOG-based brain switch was used to activate a classic row-column P300-based speller only when spelling was needed, enabling an asynchronous operation of the system. Then, the user could input sinograms by alternately performing P300 and double-blink tasks until he or she intended to stop spelling. With the incorporation of an EOG detector, the system achieved rapid sinogram input. In addition to asynchronous operation, the performance of the proposed speller was compared with that achieved by a P300-based method alone across 18 subjects. The proposed system showed a mean communication speed of approximately 2.39 sinograms per minute, an increase of 0.83 sinograms per minute compared with the P300-based method. The preliminary online performance indicated that the proposed paradigm is a very promising approach for practical Chinese sinogram input application. This system may also be expanded to users whose languages are written in logographic scripts to serve as an assistive communication tool.

Interhemispheric Brain Switching Correlates with Severity of Sleep-Disordered Breathing for Obstructive Sleep Apnea Patients

(1) Background: Alternating interhemispheric slow-wave activity during sleep is well-established in birds and cetaceans, but its investigation in humans has been largely neglected. (2) Methods: Fuzzy entropy was used to calculate a laterality index (LI) from C3 and C4 EEG channels. The subjects were grouped according to an apnoea-hypopnoea index (AHI) for statistical analyses: Group A AHI < 15 (mild); Group B 15 ≤ AHI < 30 (moderate); Group C AHI ≥ 30 (severe). The LI distribution was analysed to characterise the brain activity variation in both hemispheres, and the cross-zero switching rate was given statistical tests to find the correlations with the severity of obstructive sleep apnea and sleep states, i.e., wake (W), light sleep (LS), deep sleep (DS), and REM. (3) Results: EEG brain switching activity was observed in all sleep stages, and the LI distribution shows that, for obstructive sleep apnea patients, the interhemispheric asymmetry of brain activity is more obvious than healthy people. A one-way ANOVA revealed a significant difference of switching rate among three groups (F(2,95) = 7.23, p = 0.0012), with Group C shows the least, and also a significant difference among four sleep stages (F(3,94) = 5.09, p = 0.0026), with REM the highest. (4) Conclusions: The alternating interhemispheric activity is confirmed ubiquitous for humans during sleep, and sleep-disordered breathing intends to exacerbate the interhemispheric asymmetry.

Novel Motor Imagery-Based Brain Switch for Patients With Amyotrophic Lateral Sclerosis: A Case Study Using Two-Channel Electroencephalography

This article presents a brain switch for patients with amyotrophic lateral sclerosis (ALS). The firststage extract sample entropy (SampEn) features are from 30-channel electroencephalography (EEG) signals. Fisher's separability criterion is then performed in the second stage to select the top two EEG channels for each patient. Features of the top two channels are sent into the last stage for classification, where a motor imagery detector based on an adaptive support vector machine (ASVM) is used to keep the false positive rate (FPR) below a near-zero threshold and gain a high true positive rate (TPR). The results on five patients with ALS demonstrated the superior performance of the brain switch in both validation and follow-up sessions.

Multiple Pathways Act Together To Establish Asymmetry of the Ventral Nerve Cord in Caenorhabditis elegans.

The central nervous system of most animals is bilaterally symmetrical. Closer observation often reveals some functional or anatomical left-right asymmetries. In the nematode Caenorhabditis elegans, the most obvious asymmetry in the nervous system is found in the ventral nerve cord (VNC), where most axons are in the right axon tract. The asymmetry is established when axons entering the VNC from the brain switch from the left to the right side at the anterior end of the VNC. In genetic screens we identified several mutations compromising VNC asymmetry. This includes alleles of col-99 (encoding a transmembrane collagen), unc-52/perlecan and unc-34 (encoding the actin modulator Enabled/Vasodilator-stimulated phosphoproteins). In addition, we evaluated mutants in known axon guidance pathways for asymmetry defects and used genetic interaction studies to place the genes into genetic pathways. In total we identified four different pathways contributing to the establishment of VNC asymmetry, represented by UNC-6/netrin, SAX-3/Robo, COL-99, and EPI-1/laminin. The combined inactivation of these pathways in triple and quadruple mutants leads to highly penetrant VNC asymmetry defects, suggesting these pathways are important contributors to the establishment of VNC asymmetry in C. elegans.

Fusing Frontal and Occipital EEG Features to Detect “Brain Switch” by Utilizing Convolutional Neural Network

Flicker is the most widely used steady-state visual evoked potential (SSVEP) stimulus. In addition, checkerboard can induce steady-state motion visual evoked potential (SSMVEP) in the occipital area. More recently, the action video is proposed to simultaneously elicit SSMVEP and induce sensorimotor area activations via the mirror neuron systems through Action Observation (AO). Integration of AO with brain–computer interface (BCI) is appealing for neural rehabilitation applications. In order to make such a BCI paradigm more feasible in neural rehabilitation, it is essential to discriminate whether a user is actively engaged with the BCI, i.e. intentional control (IC) state, or not engaged, i.e. non-intentional control (NC) state. In this study, the EEG responses to these three types of visual stimuli were compared for the first time and a convolutional neural network (CNN) was proposed to discriminate IC and NC states. A visual gaiting stimulus was designed to realize BCI-based AO. The results showed that the power of alpha rhythms from frontal area decreased more when the participants engaged at the gaiting stimuli than when the participants engaged at the other two types of stimuli. In addition, the correlation coefficient between the EEG from the occipital area and the template signals increased when the participants engaged at the stimuli. The results also clearly demonstrated the proposed CNN method can discriminate the IC and NC states. In addition, the combination of the attention feature from the frontal area and the SSVEP/SSMVEP feature from the occipital area showed significant performance improvement for the gaiting stimulus, but not for the other two types of stimuli.

Direct brain-controlled multi-robot cooperation task

Brain control is a new control method. The traditional brain-controlled robot is mainly used to control a single robot to accomplish a specific task. However, the brain-controlled multi-robot cooperation (MRC) task is a new topic to be studied. This paper presents an experimental research which received the "Innovation Creative Award" in the brain-computer interface (BCI) brain-controlled robot contest at the World Robot Contest. Two effective brain switches were set: total control brain switch and transfer switch, and BCI based steady-state visual evoked potentials (SSVEP) was adopted to navigate a humanoid robot and a mechanical arm to complete the cooperation task. Control test of 10 subjects showed that the excellent SSVEP-BCI can be used to achieve the MRC task by appropriately setting up the brain switches. This study is expected to provide inspiration for the future practical brain-controlled MRC task system.

Decoding of Self-paced Lower-Limb Movement Intention: A Case Study on the Influence Factors.

Brain-machine interfaces (BMIs) have been applied as new rehabilitation tools for motor disabled individuals. Active involvement of cerebral activity has been shown to enhance neuroplasticity and thus to restore mobility. Various studies have focused on the detection of upper-limb movement intention, while the fewer study has investigated the lower-limb movement intention decoding. This study presents a BMI to decode the self-paced lower-limb movement intention, with 10 healthy subjects participating in the experiment. We varied four influence factors including the movement type (dorsiflexion or plantar flexion), the limb side (left or right leg), the processing method (time-series analysis based on MRCP, i.e., movement-related cortical potential or frequency-domain estimation based on SMR, i.e., sensory motor rhythm) and the frequency band (e.g., delta, theta, mu, beta and MRCP band at [0.1 1] Hz), to estimate both single-trial and sample-based performance. Feature analysis was then conducted to show the discriminant power (DP) and brain modulations. The average detection latency was −0.334 ± 0.216 s in single-trial basis across all conditions. An average area under the curve (AUC) of 91.0 ± 3.5% and 68.2 ± 4.6% was obtained for the MRCP-based and SMR-based method in the classification, respectively. The best performance was yielded from plantar flexion with left leg using time-series analysis on the MRCP band. The feature analysis indicated a cross-subject consistency of DP with the MRCP-based method and subject-specific variance of DP with the SMR-based method. The results presented here might be further exploited in a rehabilitation scenario. The comprehensive factor analysis might be used to shed light on the design of an effective brain switch to trigger external robotic devices.

An emergency call system for patients in locked‐in state using an SSVEP‐based brain switch

Patients in a locked-in state (LIS) due to severe neurological disorders such as amyotrophic lateral sclerosis (ALS) require seamless emergency care by their caregivers or guardians. However, it is a difficult job for the guardians to continuously monitor the patients' state, especially when direct communication is not possible. In the present study, we developed an emergency call system for such patients using a steady-state visual evoked potential (SSVEP)-based brain switch. Although there have been previous studies to implement SSVEP-based brain switch system, they have not been applied to patients in LIS, and thus their clinical value has not been validated. In this study, we verified whether the SSVEP-based brain switch system can be practically used as an emergency call system for patients in LIS. The brain switch used for our system adopted a chromatic visual stimulus, which proved to be visually less stimulating than conventional checkerboard-type stimuli but could generate SSVEP responses strong enough to be used for brain-computer interface (BCI) applications. To verify the feasibility of our emergency call system, 14 healthy participants and 3 patients with severe ALS took part in online experiments. All three ALS patients successfully called their guardians to their bedsides in about 6.56 seconds. Furthermore, additional experiments with one of these patients demonstrated that our emergency call system maintains fairly good performance even up to 4 weeks after the first experiment without renewing initial calibration data. Our results suggest that our SSVEP-based emergency call system might be successfully used in practical scenarios.

A global brain state underlies C. elegans sleep behavior.

How the brain effectively switches between and maintains global states, such as sleep and wakefulness, is not yet understood. We used brainwide functional imaging at single-cell resolution to show that during the developmental stage of lethargus, the Caenorhabditis elegans brain is predisposed to global quiescence, characterized by systemic down-regulation of neuronal activity. Only a few specific neurons are exempt from this effect. In the absence of external arousing cues, this quiescent brain state arises by the convergence of neuronal activities toward a fixed-point attractor embedded in an otherwise dynamic neural state space. We observed efficient spontaneous and sensory-evoked exits from quiescence. Our data support the hypothesis that during global states such as sleep, neuronal networks are drawn to a baseline mode and can be effectively reactivated by signaling from arousing circuits.