Maximum Information Transfer Rate Research Articles

Effect of 3D paradigm synchronous motion for SSVEP-based hybrid BCI-VR system.

A brain-computer interface (BCI) system and virtual reality (VR) are integrated as a more interactive hybrid system (BCI-VR) that allows the user to manipulate the car. A virtual scene in the VR system that is the same as the physical environment is built, and the object's movement can be observed in the VR scene. The four-class three-dimensional (3D) paradigm is designed and moves synchronously in virtual reality. The dynamic paradigm may affect their attention according to the experimenters' feedback. Fifteen subjects in our experiment steered the car according to a specified motion trajectory. According to our online experimental result, different motion trajectories of the paradigm have various effects on the system's performance, and training can mitigate this adverse effect. Moreover, the hybrid system using frequencies between 5 and 10Hz indicates better performance than those using lower or higher stimulation frequencies. The experiment results show a maximum average accuracy of 0.956 and a maximum information transfer rate (ITR) of 41.033 bits/min. It suggests that a hybrid system provides a high-performance way of brain-computer interaction. This research could encourage more interesting applications involving BCI and VR technologies.

Spintronic Eyeblink Gesture Sensor With Wearable Interface System.

This work presents an eyeblink system that detects magnets placed on the eyelid via integrated magnetic sensors and an analogue circuit on an eyewear frame (without a glass lens). The eyelid magnets were detected using tunnelling magnetoresistance (TMR) bridge sensors with a sensitivity of 14 mV/V/Oe and were positioned centre-right and centre-left of the eyewear frame. Each eye side has a single TMR sensor wired to a single circuit, where the signal was filtered (<0.5 Hz and >30 Hz) and amplified to detect the weak magnetic field produced by the 3-millimetre (mm) diameter and 0.5 mm thickness N42 Neodymium magnets attached to a medical tape strip, for the adult-age demographic. Each eyeblink was repeated by a trigger command (right eyeblink) followed by the appropriate command, right, left or both eyeblinks. The eyeblink gesture system has shown repeatability, resulting in blinking classification based on the analogue signal amplitude threshold. As a result, the signal can be scaled and classified as well as, integrated with a Bluetooth module in real-time. This will enable end-users to connect to various other Bluetooth enabled devices for wireless assistive technologies. The eyeblink system was tested by 14 participants via a stimuli-based game. Within an average time of 185-seconds, the system demonstrated a group mean accuracy of 72% for 40 commands. Moreover, the maximum information transfer rate (ITR) of the participants was 35.95 Bits per minute.

Research on Unsupervised Classification Algorithm Based on SSVEP

Filter Bank Canonical Correlation Analysis (FBCCA) is used to classify electroencephalography (EEG) signals to overcome insufficient training data for EEG signal classification. This approach is not constrained by the training data or time and also performs unsupervised Steady-State Visual Evoked Potential (SSVEP) classification in a short time, which is easy to extend and optimize. By examining the data set from the Brain–Computer Interface (BCI) contest and comparing it to Canonical Correlation Analysis (CCA) using various parameter settings, the results show that FBCCA carries better classification performance than CCA. When the number of harmonics is 4 and the number of subbands is 5, the identification rate of 40 targets with the frequency difference of 0.2 Hz achieves 88.9%, and the maximum information transfer rate (ITR) achieves 88.64 bits/min, which shows superior compatibility and practicability.

The effect of stimulus number on the recognition accuracy and information transfer rate of SSVEP–BCI in augmented reality

Objective. The biggest advantage of steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) lies in its large command set and high information transfer rate (ITR). Almost all current SSVEP–BCIs use a computer screen (CS) to present flickering visual stimuli, which limits its flexible use in actual scenes. Augmented reality (AR) technology provides the ability to superimpose visual stimuli on the real world, and it considerably expands the application scenarios of SSVEP–BCI. However, whether the advantages of SSVEP–BCI can be maintained when moving the visual stimuli to AR glasses is not known. This study investigated the effects of the stimulus number for SSVEP–BCI in an AR context. Approach. We designed SSVEP flickering stimulation interfaces with four different numbers of stimulus targets and put them in AR glasses and a CS to display. Three common recognition algorithms were used to analyze the influence of the stimulus number and stimulation time on the recognition accuracy and ITR of AR–SSVEP and CS–SSVEP. Main results. The amplitude spectrum and signal-to-noise ratio of AR–SSVEP were not significantly different from CS–SSVEP at the fundamental frequency but were significantly lower than CS–SSVEP at the second harmonic. SSVEP recognition accuracy decreased as the stimulus number increased in AR–SSVEP but not in CS–SSVEP. When the stimulus number increased, the maximum ITR of CS–SSVEP also increased, but not for AR–SSVEP. When the stimulus number was 25, the maximum ITR (142.05 bits min−1) was reached at 400 ms. The importance of stimulation time in SSVEP was confirmed. When the stimulation time became longer, the recognition accuracy of both AR–SSVEP and CS–SSVEP increased. The peak value was reached at 3 s. The ITR increased first and then slowly decreased after reaching the peak value. Significance. Our study indicates that the conclusions based on CS–SSVEP cannot be simply applied to AR–SSVEP, and it is not advisable to set too many stimulus targets in the AR display device.

A user-friendly SSVEP-based BCI using imperceptible phase-coded flickers at 60Hz

A brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) was developed by four-class phase-coded stimuli. SSVEPs elicited by flickers at 60Hz, which is higher than the critical fusion frequency (CFF), were compared with those at 15Hz and 30Hz. SSVEP components in electroencephalogram (EEG) were detected using task related component analysis (TRCA) method. Offline analysis with 17 subjects indicated that the highest information transfer rate (ITR) was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%. The online BCI system reached an averaged classification accuracy of 87.75±3.50% at 60Hz with 4s, resulting in an ITR of 16.73±1.63bpm. In particular, the maximum ITR for a subject was 80bpm with 0.5s at 60Hz. Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz, the results of the behavioral test indicated that, with no perception of flicker, the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz. Correlation analysis revealed that SSVEP with higher signal-to-noise ratio (SNR) corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance. This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.

Robust asynchronous control of ERP-Based brain-Computer interfaces using deep learning

Background and Objective. Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requiring the intervention of a supervisor when the user wishes to turn his attention away from the BCI system. In order to bring these BCIs into real-life applications, a robust asynchronous control of the system is required through monitoring of user attention. Despite the great importance of this limitation, which prevents the deployment of these systems outside the laboratory, it is often overlooked in research articles. This study was aimed to propose a novel method to solve this problem, taking advantage of deep learning for the first time in this context to overcome the limitations of previous strategies based on hand-crafted features. Methods. The proposed method, based on EEG-Inception, a novel deep convolutional neural network, divides the problem in 2 stages to achieve the asynchronous control: (i) the model detects user’s control state, and (ii) decodes the command only if the user is attending to the stimuli. Additionally, we used transfer learning to reduce the calibration time, even exploring a calibration-less approach. Results. Our method was evaluated with 22 healthy subjects, analyzing the impact of the calibration time and number of stimulation sequences on the system’s performance. For the control state detection stage, we report average accuracies above 91% using only 1 sequence of stimulation and 30 calibration trials, reaching a maximum of 96.95% with 15 sequences. Moreover, our calibration-less approach also achieved suitable results, with a maximum accuracy of 89.36%, showing the benefits of transfer learning. As for the overall asynchronous system, which includes both stages, the maximum information transfer rate was 35.54 bpm, a suitable value for high-speed communication. Conclusions. The proposed strategy achieved higher performance with less calibration trials and stimulation sequences than former approaches, representing a promising step forward that paves the way for more practical applications of ERP-based spellers.

How to build a fast and accurate code-modulated brain-computer interface

Objective. In the last decade, the advent of code-modulated brain-computer interfaces (BCIs) has allowed the implementation of systems with high information transfer rates (ITRs) and increased the possible practicality of such interfaces. In this paper, we evaluate the effect of different numbers of targets in the stimulus display, modulation sequences generators, and signal processing algorithms on the accuracy and ITR of code-modulated BCIs. Approach. We use both real and simulated electroencephalographic (EEG) data, to evaluate these parameters and methods. Then, we compared numerous different setups to assess their performance and identify the best configurations. We also evaluated the dependability of our simulated evaluation approach. Main results. Our results show that Golay, almost perfect, and deBruijn sequence-based visual stimulus modulations provide the best results, significantly outperforming the commonly used m-sequences in all cases. We conclude that artificial neural network processing algorithms offer the best processing pipeline for this type of BCI, achieving a maximum classification accuracy of 94.7% on real EEG data while obtaining a maximum ITR of 127.2 bits min−1 in a simulated 64-target system. Significance. We used a simulated framework that demonstrated previously unattainable flexibility and convenience while staying reasonably realistic. Furthermore, our findings suggest several new considerations which can be used to guide further code-based BCI development.

Analysis of the Information Transfer Rate-ITR in Linear and Non-linear Feature Extraction Methods for SSVEP Signals

The most popular paradigm in BCIs is the steady-state visually evoked potential (SSVEP) due to their advantages, such as the high information transfer rate (ITR), the time spent on users in the training phase, and the capacity to discriminate each stimulus. One of the most influential factors in the ITR evaluation is the feature extraction methods since these can increase the accuracy. Here, we compare nine methods for the extraction of features from SSVEP signals to identify those with better performance, according to the time window (TW), its technology (equipment and number of nodes), and the value of ITR. The study identifies two groups: the first one is characterized by presenting variations of correlated component analysis (CCA), which is highly used to increase the ITR due to its efficiency in classification and its capacity of response to reduction (TW), such as MsetCCA, IT-CCA, FBCCA; the second one are the representation special based methods that consider the non-linear nature of the electroencephalogram (EEG) signal such as TRCA, CORRCA, EMD, and VMD. The results show a considerable difference between these groups. The maximum ITR value for FBCCA was 117.75 [bits/min] in a TW of 1.25s, while the VMD method achieved 3120 [bits/min] in a TW of 1s, respectively. The comparison covers signals between 0.55 and 8 seconds, taking into account visual strain, the experimental environment, and other artifacts.

Evaluation of a P300-Based Brain-Machine Interface for a Robotic Hand-Orthosis Control.

This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the ALS patients. For the BMI online test, each subject performed from 6 to 36 attempts of target selections using the interface. In this case, around 46% of the participants obtained 100% of accuracy, and the average online accuracy was 89.83%. The maximum information transfer rate (ITR) observed in the experiments was 52.83 bit/min, whereas that the average ITR was 18.13 bit/min. The contributions of this work are the following. First, we report the development and evaluation of a mind-controlled robotic hand-orthosis for patients with ALS. To our knowledge, this BMI is one of the first P300-based assistive robotic devices with multiple targets evaluated on people with ALS. Second, we provide a database with calibration data and online EEG recordings obtained in the evaluation of our BMI. This data is useful to develop and compare other BMI systems and test the processing pipelines of similar applications.

DEVELOPMENT AND RESEARCH OF A WIRELESS CONTROL SYSTEM FOR DEVICE “BIONIC EAR”

Context. The analysis of reliability indicators of information transfer between elements of wireless control systems – a control device implemented on a smartphone and a communication processor of the developed hearing aid is carried out. The object of research is the reliability indicators of the wireless control channel. The subject of the study is a theoretical and experimental analysis of the dependencies of reliability indicators of the designed wireless control system. Objective. The purpose of the work is to determine and study the analytical and experimental dependences of the reliability of transmission over the wireless control channel on the properties of the communication channel and settings of the system elements, forming recommendations for setting the parameters of the elements of the control system.Methods. The elements of the reliability theory are used to determine the dependences of the bit and block error coefficients on the properties of the communication channel of the configuration of the elements of the control system. Analytical relationships are obtained to determine the reliability of transmission taking into account possible signal distortions. The dependences of reliability indicators on the parameters of the elements of the control system are investigated, illustrative examples are given. Simulated circuit models of a control system with selected Bluetooth wireless technology have been developed. Experimental studies have been carried out, on the basis of the data obtained, conclusions have been drawn and recommendations have been made on the choice of control system configurations in order to ensure specified reliability indicators at maximum efficiency (information transfer rate). Results. The dependences of reliability indicators (bit and block error coefficients) on the properties of the wireless information transmission channel and the parameters of the control system elements are obtained. Recommendations are given on the use of the results obtained when choosing the settings for the control element (smartphone) and the controlled element (communication processor of the hearing aid). To conduct experimental research, control system models in the MathWorks MatLab Simulink environment were created and tuned. Conclusions. The studies carried out in the work allow us to calculate and reasonably choose the parameters of the devices of the wireless control system for the given reliability indicators taking into account the error behavior model in the transmission channel and the settings of the associated equipment. This makes it possible to design and implement reliable control systems with specified reliability indicators and maximum information transfer rate.

Оптимизация параметров кода и режима обработки сигналов в условиях преднамеренных помех

In this article we study the analysis and optimization principles of parameters and the mode of use the error-correcting code in the information transmission system with a reverse channel, which functions under the influence of intentional interference, its structure can be selected from the position of violation (deterioration) of the system operating. We see the developed methodology for optimizing system parameters according to the criterion of the maximum guaranteed information transfer rate for interference classes with limited average power. An assessment of the efficiency gain provided by applying the proposed optimizing methods of the channel block structure and its decoding mode is made.

Analyzing text recognition from tactually evoked EEG.

Tactual exploration of objects produce specific patterns in the human brain and hence objects can be recognized by analyzing brain signals during tactile exploration. The present work aims at analyzing EEG signals online for recognition of embossed texts by tactual exploration. EEG signals are acquired from the parietal region over the somatosensory cortex of blindfolded healthy subjects while they tactually explored embossed texts, including symbols, numbers, and alphabets. Classifiers based on the principle of supervised learning are trained on the extracted EEG feature space, comprising three features, namely, adaptive autoregressive parameters, Hurst exponents, and power spectral density, to recognize the respective texts. The pre-trained classifiers are used to classify the EEG data to identify the texts online and the recognized text is displayed on the computer screen for communication. Online classifications of two, four, and six classes of embossed texts are achieved with overall average recognition rates of 76.62, 72.31, and 67.62% respectively and the computational time is less than 2s in each case. The maximum information transfer rate and utility of the system performance over all experiments are 0.7187 and 2.0529bits/s respectively. This work presents a study that shows the possibility to classify 3D letters using tactually evoked EEG. In future, it will help the BCI community to design stimuli for better tactile augmentation n also opens new directions of research to facilitate 3D letters for visually impaired persons. Further, 3D maps can be generated for aiding tactual BCI in teleoperation.

Quantification of information transfer rate of the human hand during a mouse clicking task with healthy adults and one adult with Duchenne muscular Dystrophy.

Duchenne muscular Dystrophy (DMD) Is a progressive muscle degenerative disease. Active hand assistive devices, can improve the quality of life of people with DMD. Such devices show a rejection rate due to complexity. Our hypothesis is, that a simple orthosis might prove more functional and realistic in assisting people with DMD. To investigate, we developed a portable setup that provides various visual stimuli and records the response of the subjects' fingers through a mouse clicking task. Six LEDs served as visual stimuli. The subjects' responses were obtained through mechanical interaction with two vertical mice. Different combinations of frequencies and numbers of stimuli were tested with 8 healthy subjects and one with DMD. Performance was evaluated in terms of information transfer rate (ITR), pattern accuracy and perceived workload. The outcome shows that lower complexity results in lower ITR and lower workload for all subjects. While for healthy subjects, maximum ITR was 4.3 bits/s, for DMD maximum ITR was 2.5 bits/s. Both maxima were achieved at the same trial (3 fingers at 2 Hz). This trial agrees with a pareto optimization analysis of ITR with respect to workload. The results support our hypothesis for a simple yet functional solution. Furthermore healthy subjects and the individual with DMD, in principal show similar finger control, albeit with lower absolute performance.

Using sEMG, EOG and VOG to Control an Intelligent Environment

Abstract: People with disabilities have many difficulties to interact with daily devices, mostly because of inherent limitations of their deficiency. Simple activities like turning lights, TV, fan or any other device on can be even impossible for these people. In this study, the problem of interaction with an intelligent environment through two paradigms of Human-Computer Interface (HCI) was addressed. The first one uses surface electromyography (sEMG) and electrooculography (EOG), caught by an EEG/sEMG device (Emotiv EPOC), and the second one uses videooculography (VOG) caught by an eye tracker (Eye Tribe Tracker). Results from both paradigms were compared using two metrics: Information Transfer Rate (ITR) and Utility, obtaining maximum ITR of 84.37 bits/min and Utility of 37.86 bits/min.

Maximizing information transfer rates in an SSVEP-based BCI using individualized Bayesian probability measures.

Successful brain-computer interfaces (BCIs) swiftly and accurately communicate the user's intention to a computer. Typically, information transfer rate (ITR) is used to measure the performance of a BCI. We propose a multi-step process to speed up detection and classification of the user's intent and maximize ITR. Users randomly looked at 4 frequency options on the interface in two sessions, one without and one with performance feedback. Analysis was performed off-line. A ratio of the canonical correlation analysis (CCA) coefficients was used to construct a Bayesian probability model and a thresholding method for the ratio of the posterior probability of the target frequency over maximal posterior probability of non-target frequencies was used as classification criteria. Moreover, the probability thresholds were optimized for each frequency, subject to maximizing the ITR. We achieved a maximum ITR of 39.82 bit/min. Although the performance feedback did not improve the overall ITR, it did improve the accuracy measure. Possible applications in the medical industry are discussed.

Behavioral evidence for the role of cortical θ oscillations in determining auditory channel capacity for speech.

Studies on the intelligibility of time-compressed speech have shown flawless performance for moderate compression factors, a sharp deterioration for compression factors above three, and an improved performance as a result of “repackaging”—a process of dividing the time-compressed waveform into fragments, called packets, and delivering the packets in a prescribed rate. This intricate pattern of performance reflects the reliability of the auditory system in processing speech streams with different information transfer rates; the knee-point of performance defines the auditory channel capacity. This study is concerned with the cortical computation principle that determines channel capacity. Oscillation-based models of speech perception hypothesize that the speech decoding process is guided by a cascade of oscillations with theta as “master,” capable of tracking the input rhythm, with the theta cycles aligned with the intervocalic speech fragments termed θ-syllables; intelligibility remains high as long as theta is in sync with the input, and it sharply deteriorates once theta is out of sync. In the study described here the hypothesized role of theta was examined by measuring the auditory channel capacity of time-compressed speech undergone repackaging. For all speech speeds tested (with compression factors of up to eight), packaging rate at capacity equals 9 packets/s—aligned with the upper limit of cortical theta, θmax (about 9 Hz)—and the packet duration equals the duration of one uncompressed θ-syllable divided by the compression factor. The alignment of both the packaging rate and the packet duration with properties of cortical theta suggests that the auditory channel capacity is determined by theta. Irrespective of speech speed, the maximum information transfer rate through the auditory channel is the information in one uncompressed θ-syllable long speech fragment per one θmax cycle. Equivalently, the auditory channel capacity is 9 θ-syllables/s.

Generating visual flickers for eliciting robust steady-state visual evoked potentials at flexible frequencies using monitor refresh rate.

In the study of steady-state visual evoked potentials (SSVEPs), it remains a challenge to present visual flickers at flexible frequencies using monitor refresh rate. For example, in an SSVEP-based brain-computer interface (BCI), it is difficult to present a large number of visual flickers simultaneously on a monitor. This study aims to explore whether or how a newly proposed frequency approximation approach changes signal characteristics of SSVEPs. At 10 Hz and 12 Hz, the SSVEPs elicited using two refresh rates (75 Hz and 120 Hz) were measured separately to represent the approximation and constant-period approaches. This study compared amplitude, signal-to-noise ratio (SNR), phase, latency, scalp distribution, and frequency detection accuracy of SSVEPs elicited using the two approaches. To further prove the efficacy of the approximation approach, this study implemented an eight-target BCI using frequencies from 8–15 Hz. The SSVEPs elicited by the two approaches were found comparable with regard to all parameters except amplitude and SNR of SSVEPs at 12 Hz. The BCI obtained an averaged information transfer rate (ITR) of 95.0 bits/min across 10 subjects with a maximum ITR of 120 bits/min on two subjects, the highest ITR reported in the SSVEP-based BCIs. This study clearly showed that the frequency approximation approach can elicit robust SSVEPs at flexible frequencies using monitor refresh rate and thereby can largely facilitate various SSVEP-related studies in neural engineering and visual neuroscience.

Introducing the tactile speller: an ERP-based brain–computer interface for communication

In this study, a tactile speller was developed and compared with existing visual speller paradigms in terms of classification performance and elicited event-related potentials (ERPs). The fingertips of healthy participants were stimulated with short mechanical taps while electroencephalographic activity was measured. The letters of the alphabet were allocated to different fingers and subjects could select one of the fingers by silently counting the number of taps on that finger. The offline and online performance of the tactile speller was compared to the overt and covert attention visual matrix speller and the covert attention Hex-o-Spell speller. For the tactile speller, binary target versus non-target classification accuracy was 67% on average. Classification and decoding accuracies of the tactile speller were lower than the overt matrix speller, but higher than the covert matrix speller, and similar to Hex-o-Spell. The average maximum information transfer rate of the tactile speller was 7.8 bits min−1 (1.51 char min−1), with the best subject reaching a bit-rate of 27 bits min−1 (5.22 char min−1). An increased amplitude of the P300 ERP component was found in response to attended stimuli versus unattended stimuli in all speller types. In addition, the tactile and overt matrix spellers also used the N2 component for discriminating between targets and non-targets. Overall, this study shows that it is possible to use a tactile speller for communication. The tactile speller provides a useful alternative to the visual speller, especially for people whose eye gaze is impaired.

Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces

Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.

A high-speed BCI based on code modulation VEP

Recently, electroencephalogram-based brain–computer interfaces (BCIs) have attracted much attention in the fields of neural engineering and rehabilitation due to their noninvasiveness. However, the low communication speed of current BCI systems greatly limits their practical application. In this paper, we present a high-speed BCI based on code modulation of visual evoked potentials (c-VEP). Thirty-two target stimuli were modulated by a time-shifted binary pseudorandom sequence. A multichannel identification method based on canonical correlation analysis (CCA) was used for target identification. The online system achieved an average information transfer rate (ITR) of 108 ± 12 bits min−1 on five subjects with a maximum ITR of 123 bits min−1 for a single subject.