Brain-computer Interface Experiment Research Articles

An Instant Donning Multi-Channel EEG Headset (with Comb-Shaped Dry Electrodes) and BCI Applications.

We developed a new type of electroencephalogram (EEG) headset system with comb-shaped electrodes that enables the wearer to quickly don and utilize it in daily life. Two models that can measure EEG signals using up to eight channels have been implemented. The electrodes implemented in the headsets are similar to a comb and are placed quickly by wiping the hair (as done with a comb) using the headset. To verify this headset system, donning time was measured and three brain computer interface (BCI) application experiments were conducted. Alpha rhythm-based, steady-state visual evoked potential (SSVEP)-based, and auditory steady state response (ASSR)-based BCI systems were adopted for the validation experiments. Four subjects participated and ten trials were repeated in the donning experiment. The results of the validation experiments show that reliable EEG signal measurement is possible immediately after donning the headsets without any preparation. It took approximately 10 s for healthy subjects to don the headsets, including an earclip with reference and ground electrodes. The results of alpha rhythm-based BCI showed 100% accuracy. Furthermore, the results of SSVEP-based and ASSR-based BCI experiments indicate that performance is sufficient for BCI applications; 95.7% and 76.0% accuracies were obtained, respectively. The results of BCI paradigm experiments indicate that the new headset type is feasible for various BCI applications.

Multimodal Fuzzy Fusion for Enhancing the Motor-Imagery-Based Brain Computer Interface

Brain-computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery-based brain-computer interfaces are popular because they avoid unnecessary external stimuli. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain-computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain-computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain-computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain-computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain-computer interface commands.

A Brain-Computer Interface Based on Three-Dimensional Stereo Stimuli for Assisting Clinical Object Recognition Assessment in Patients With Disorders of Consciousness.

The coma recovery scale-revised (CRS-R) behavioral scale is commonly used for the clinical evaluation of patients with disorders of consciousness (DOC). However, since DOC patients generally cannot supply stable and efficient behavioral responses to external stimulation, evaluation results based on behavioral scales are not sufficiently accurate. In this paper, we proposed a novel brain-computer interface (BCI) based on 3D stereo audiovisual stimuli to supplement object recognition evaluation in the CRS-R. During the experiment, subjects needed to follow the instructions and to focus on the target object on the screen, whereas EEG data were recorded and analyzed in real time to determine the object of focus, and the detection result was output as feedback. Thirteen DOC patients participated in the object recognition assessments using the 3D audiovisual BCI and CRS-R. None of the patients showed object recognition function in the CRS-R assessment before the BCI experiment. However, six of these DOC patients achieved accuracies that were significantly higher than the chance level in the BCI-based assessment, indicating the successful detection of object recognition function in these six patients using our 3D audiovisual BCI system. These results suggest that the BCI method may provide a more sensitive object recognition evaluation compared with CRS-R and may be used to assist clinical CRS-R for DOC patients.

The Analysis Of Motor Imagery And SSVEPs For the BCI Application

Many studies have shown that the electrical and magnetic fields generated during brain activities can produce certain signals. Some of these signals can be captured using electroencephalography, a detection tool involving mobile brainwave sensors whose use has matured and become affordable. The brain-computer interface (BCI) provides an alternative form of communication between the human and a system (computer or actuator) without any physical contact between them. There are many ways to evoke brain signals for translation into computer tasks, but the most popular are motor imagery and steady-state visual evoked potentials (SSVEPs). In this research, an offline analysis of motor imagery and SSVEPs based on BCI experiments that use electroencephalography (EEG) is reported. The results show that SSVEPs are more accurate and convenient than motor imagery, with errors of 15 percent and 35 percent, respectively.

Performance Prediction for a Near-Infrared Spectroscopy-Brain-Computer Interface Using Resting-State Functional Connectivity of the Prefrontal Cortex.

One of the most important issues in current brain-computer interface (BCI) research is the prediction of a user's BCI performance prior to the main BCI session because it would be useful to reduce the time required to determine the BCI paradigm best suited to that user. In electroencephalography (EEG)-BCI research, whether a user has low BCI performance toward a specific BCI paradigm has been estimated using a variety of resting-state EEG features. However, no previous study has attempted to predict the performance of near-infrared spectroscopy (NIRS)-BCI using resting-state NIRS data recorded before the main BCI experiment. In this study, we investigated whether the performance of an NIRS-BCI discriminating a mental arithmetic task from the baseline state could be predicted using resting-state functional connectivity (RSFC) of the prefrontal cortex. The investigation of NIRS signals recorded from 29 participants revealed that the RSFC between bilateral channels in the prefrontal area was negatively correlated with subsequent BCI performance (e.g. a fitted line for the RSFC between L2 and R2 channels explains 41% of BCI performance variation). We expect that our indicator can be used to predict BCI performance of an individual user prior to the main NIRS-BCI experiments, thereby facilitating implementation of more efficient NIRS-BCI systems.

An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation

BackgroundCorticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving brain-computer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system. New methodIn this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment. ResultsThe classification accuracy of the CBPT-based BCI system was found to be 92.81 ± 2.09% for the healthy experimental group and 84.53 ± 4.58% for the patients’ group. Comparison with existing methodThe CBPT method significantly (p-value < 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy. ConclusionsThe experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms.

Null Ain’t Dull: New Perspectives on Motor Cortex

Classical work has viewed primary motor cortex (M1) as a controller of muscle and body dynamics. A recent brain-computer interface (BCI) experiment suggests a new, complementary perspective: M1 is itself a dynamical system under active control of other circuits.

A multi-target brain-computer interface based on code modulated visual evoked potentials.

The number of selectable targets is one of the main factors that affect the performance of a brain-computer interface (BCI). Most existing code modulated visual evoked potential (c-VEP) based BCIs use a single pseudorandom binary sequence and its circularly shifting sequences to modulate different stimulus targets, making the number of selectable targets limited by the length of modulation codes. This paper proposes a novel paradigm for c-VEP BCIs, which divides the stimulus targets into four target groups and each group of targets are modulated by a unique pseudorandom binary code and its circularly shifting codes. Based on the paradigm, a four-group c-VEP BCI with a total of 64 stimulus targets was developed and eight subjects were recruited to participate in the BCI experiment. Based on the experimental data, the characteristics of the c-VEP BCI were explored by the analyses of auto- and cross-correlation, frequency spectrum, signal to noise ratio and correlation coefficient. On the basis, single-trial data with the length of one stimulus cycle were classified and the attended target was recognized. The averaged classification accuracy across subjects was 88.36% and the corresponding information transfer rate was as high as 184.6 bit/min. These results suggested that the c-VEP BCI paradigm is both feasible and effective, and provides a new solution for BCI study to substantially increase the number of available targets.

Detection of Highly Motivated Time Segments in Brain Computer Interface Signals

ABSTRACTMotivation of a subject, who is associated with the data acquisition of brain computer interface (BCI) experiment, is a very crucial parameter for executing a successful BCI application. This paper proposes a novel method to present the distribution of motivation of a subject during a BCI experiment. The proposed method was successfully applied to the BCI Competition 2003 Data Set III and the BCI Competition 2005 Data Set I using fast Fourier transform-based band power features with a linear discriminant analysis classifier. The results show that not only the motivation of the subject dramatically changes during the trial but also using highly motivated time segments provides 7.86% and 2.00% improvement in the classification accuracy of the BCI Competition 2003 Data Set III and the BCI Competition 2005 Data Set I, respectively.

Design and Implementation of Real-time Brain-computer Interface System Based on LabVIEW

Brain-computer interface (BCI) is a newly developed man-machine interface method that is applied in the field of brain science, rehabilitation engineering, biomedical engineering and human-machine automatic control. Under the LabVIEW environment of virtual instrument graphical programming language, this paper improves the program of Active One, a physiological signal acquisition system. The off-line processing system of electroencephalogram(EEG) is improved to a real-time processing system and a real-time feedback BCI experiment system is established based on visual evoked potential (VEP). The cumulative average method combined with the FIR filter method was used to extract the VEP signal and form a closed-loop BCI. Real-time feedback of the BCI system was realized and the VEP signal was automatically identified. Using the proposed method of this paper to implement BCI can achieve a higher accuracy with certain practicality and feasibility.

Open Access Dataset for EEG+NIRS Single-Trial Classification.

We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we con-ducted two BCI experiments (left vs. right hand motor imagery; mental arithmetic vs. resting state). The dataset was validated using baseline signal analysis methods, with which classification performance was evaluated for each modality and a combination of both modalities. As already shown in previous literature, the capability of discriminating different mental states can be en-hanced by using a hybrid approach, when comparing to single modality analyses. This makes the provided data highly suitable for hybrid BCI investigations. Since our open access dataset also comprises motion artifacts and physiological data, we expect that it can be used in a wide range of future validation approaches in multimodal BCI research.

A 2.048 Mb/s Full-Duplex Free-Space Optical Transceiver IC for a Real-Time &lt;italic&gt;In Vivo&lt;/italic&gt; Brain–Computer Interface Mouse Experiment Under Social Interaction

This paper presents the first free-space optical transceiver IC for social, psychological brain–computer interface experiments with multiple mice. The proposed IC embedded in a head-mounted module (HMM) includes 8-channel neural recorder and a neural stimulator in order to record extracellular action potentials and local field potentials while injecting diverse waveforms of monophasic intracranial stimulation current into the medial forebrain bundle for the activation of the nervous system. The HMM implanted on a mouse transmits the neural recording data to an optical base station by using a mandatory 591/624-nm (amber/red) visible behavior tracking LED as an optical transmitter to save power. The HMM is controlled simultaneously via a 940 nm infrared optical downlink to reduce interference to the visible optical uplink. The proposed IC fabricated in a 0.18- $\mu \text{m}$ HV BCDMOS process consumes 90 $\mu \text{W}$ while achieving a 2.048-Mb/s transmission rate.

Open Ephys electroencephalography (Open Ephys + EEG): a modular, low-cost, open-source solution to human neural recording

Objective. Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Approach. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys + EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. Main results. The Open Ephys + EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8–14 Hz activity between the Open Ephys + EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Significance. Open Ephys + EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

Event-related potential data from a guess the number brain-computer interface experiment on school children

Guess the number is a simple P300-based brain-computer interface experiment. Its aim is to ask the measured participant to pick a number between 1 and 9. Then, he or she is exposed to corresponding visual stimuli and experimenters try to guess the number thought while they are observing event-related potential waveforms on-line. 250 school-age children participated in the experiments that were carried out in elementary and secondary schools in the Czech Republic. Electroencephalographic data from three EEG channels (Fz, Cz, Pz) and stimuli markers were stored. Additional metadata about the participants were collected (gender, age, laterality, the number thought by the participant, the guess of the experimenters, and various interesting additional information). Consequently, we offer the largest publicly available odd-ball paradigm collection of datasets to neuroscientific and brain-computer interface community.

Optimized Bi-Objective EEG Channel Selection and Cross-Subject Generalization With Brain–Computer Interfaces

Electroencephalography (EEG) signal processing to decode motor imagery (MI) involves high-dimensional features, which increases the computational complexity. To reduce this computational burden due to the large number of channels, an iterative multiobjective optimization for channel selection (IMOCS) is proposed in this paper. For a given MI classification task, the proposed method initializes a reference candidate solution and subsequently finds a set of the most relevant channels in an iterative manner by exploiting both the anatomical and functional relevance of EEG channels. The proposed approach is evaluated on the Wadsworth dataset for the right fist versus left fist MI tasks, while considering the cross-validation accuracy as the performance evaluation criteria. Furthermore, 12 other dimension reduction and channel selection algorithms are used for benchmarking. The proposed approach (IMOCS) achieved an average classification accuracy of about 80% when evaluated using 35 best-performing subjects. One-way analysis of variance revealed the statistical significance of the proposed approach with at least 7% improvement over other benchmarking algorithms. Furthermore, a cross-subject generalization of channel selection on untrained subjects shows that the subject-independent channels perform as good as using all channels achieving an average classification accuracy of 61%. These results are promising for the online brain–computer interface (BCI) paradigm that requires low computational complexity and also for reducing the preparation time while conducting multiple session BCI experiments for a larger pool of subjects.

An audiovisual BCI system for assisting clinical communication assessment in patients with disorders of consciousness: a case study.

The JFK Coma Recovery Scale-Revised (JFK CRS-R), a behavioral scale, is often used for clinical assessments of patients with disorders of consciousness (DOC), such as patients in a vegetative state. However, there has been a high rate of clinical misdiagnosis with the JFK CRS-R because patients with severe brain injures cannot provide sufficient behavioral responses. It is particularly difficult to evaluate the communication function in DOC patients using the JFK CRS-R because a higher level of behavioral responses is needed for communication assessments than for many other assessments, such as an auditory startle assessment. Brain-computer interfaces (BCIs), which provide control and communication by detecting changes in brain signals, can be used to evaluate patients with DOC without the need of behavioral expressions. In this paper, we proposed an audiovisual BCI system to supplement the JFK CRS-R in assessing the communication ability of patients with DOC. In the graphic user interface of the BCI system, two word buttons ("Yes" and "No" in Chinese) were randomly displayed in the left and right sides and flashed in an alternating manner. When a word button flashed, its corresponding spoken word was broadcast from an ipsilateral headphone. The use of semantically congruent audiovisual stimuli improves the detection performance of the BCI system. Similar to the JFK CRS-R, several situation-orientation questions were presented one by one to patients with DOC. For each question, the patient was required to provide his/her answer by selectively focusing on an audiovisual stimulus (audiovisual "Yes" or "No"). As a case study, we applied our BCI system in a patient with DOC who was clinically diagnosed as being in a minimally conscious state (MCS). According to the JFK CRS-R assessment, this patient was unable to communicate consistently. However, he achieved a high accuracy of 86.5% in our BCI experiment. This result indicates his reliable communication ability and demonstrates the effectiveness of our system.

ECoG data analyses to inform closed-loop BCI experiments for speech-based prosthetic applications.

Brain Computer Interfaces (BCIs) assist individuals with motor disabilities by enabling them to control prosthetic devices with their neural activity. Performance of closed-loop BCI systems can be improved by using design strategies that leverage structured and task-relevant neural activity. We use data from high density electrocorticography (ECoG) grids implanted in three subjects to study sensory-motor activity during an instructed speech task in which the subjects vocalized three cardinal vowel phonemes. We show how our findings relate to the current understanding of speech physiology and functional organization of human sensory-motor cortex. We investigate the effect of behavioral variations on parameters and performance of the decoding model. Our analyses suggest experimental design strategies that may be critical for speech-based BCI performance.

Effect of Brain Computer Interface (BCI) in stress induced loss of cognition in hippocampus of wister albino rats

Background: Brain-computer interface (BCI) is a collaboration between a brain and a device that enables signals from the brain to direct some external activity, which interface enables a direct communications pathway between the brain and the object to be controlled. By reading signals from an array of neurons and using computer chips and programs to translate the signals into action. The Magneto encephalography (MEG) is to record the firing of the neuron and absorb the brain activity as the magnetic field travels from region to region with in the brain. It has the potential to enhance the life style of the disabled person. The MEG Brain Computer Interface (BCI) impact on medicine and healthcare now may be subtle but is still revolutionary. Objectives: The purpose of this study was to develop novel methods and systems for rehabilitation and control of assistive devices using signals from the brain – BCI on motor coding and on neural plasticity. Materials and methods: The experiment was carried out in 24 albino rats with Magneto encephalography (MEG). All the rats underwent BCI procedure except the first and second group which was exposed to fake intervention. The objective of this proof of concept closed loop BCI experiment was for the subject to control the positive movement of a rat in the radial arm maze to take the food even altered position which was recorded in the system. Results: An important finding in the present study was the enhancing effect of BCI against neurodegeneration. It shows that these rats achieve to learn the task. It suggests that the BCI activated the neurons in the hippocampus and makes it sufficient for normal acquisition. Conclusion: Stress induced hippocampal degeneration leads to significant impairment of cognitive functions especially in calculation, immediate recall and attention. In this present study, stress induced loss of cognition was studied by activating the neurons of the hippocampus by grid electrodes of BCI system. Keywords: Magneto encephalography (MEG), glucocorticoid, hippocampal formation & grid electrodes.

An FDES-Based Shared Control Method for Asynchronous Brain-Actuated Robot.

The asynchronous brain-computer interface (BCI) offers more natural human-machine interaction. However, it is still considered insufficient to control rapid and complex sequences of movements for a robot without any advanced control method. This paper proposes a new shared controller based on the supervisory theory of fuzzy discrete event system (FDES) for brain-actuated robot control. The developed supervisory theory allows the more reliable control mode to play a dominant role in the robot control which is beneficial to reduce misoperation and improve the robustness of the system. The experimental procedures consist of real-time direct manual control and BCI control tests from ten volunteers. Both tests have shown that the proposed method significantly improves the performance and robustness of the robotic control. In an online BCI experiment, eight of the participants successfully controlled the robot to circumnavigate obstacles and reached the target with a three mental states asynchronous BCI while the other two participants failed in all the BCI control sessions. Furthermore, the FDES-based shared control method also helps to reduce the workload. It can be stated that the asynchronous BCI, in combination with FDES-based shared controller, is feasible for the real-time and robust control of robotics.

Using Actual and Imagined Walking Related Desynchronization Features in a BCI.

Recently, brain-computer interface (BCI) research has extended to investigate its possible use in motor rehabilitation. Most of these investigations have focused on the upper body. Only few studies consider gait because of the difficulty of recording EEG during gross movements. However, for stroke patients the rehabilitation of gait is of crucial importance. Therefore, this study investigates if a BCI can be based on walking related desynchronization features. Furthermore, the influence of complexity of the walking movements on the classification performance is investigated. Two BCI experiments were conducted in which healthy subjects performed a cued walking task, a more complex walking task (backward or adaptive walking), and imagination of the same tasks. EEG data during these tasks was classified into walking and no-walking. The results from both experiments show that despite the automaticity of walking and recording difficulties, brain signals related to walking could be classified rapidly and reliably. Classification performance was higher for actual walking movements than for imagined walking movements. There was no significant increase in classification performance for both the backward and adaptive walking tasks compared with the cued walking tasks. These results are promising for developing a BCI for the rehabilitation of gait.