Mean Information Transfer Rate Research Articles

Analysis of SSVEP component acquisition from EEG signals for efficient target identification

The application of the brain-computer interface (BCI) is massively helpful and advantageous for disabled people. Moreover, BCI is an arrangement of software and hardware interface that provides a direct interaction between the human brain and computer devices. Therefore, in this article, A steady state visual evoked potential (SSVEP)-based BCI system is presented to identify SSVEP components from multi-channel electroencephalogram (EEG) data by minimizing background noise using an adaptive spatial filtering method. Here, the proposed adaptive spatial filtering-based SSVEP component extraction (ASFSCE) model improves reproducibility among multiple trails and identifies targets efficiently by optimizing the Eigenvalue problem. Along with that, the proposed ASFSCE model minimizes computational complexity from O(G<sup>2</sup>) to to get high target identification accuracy with faster execution. Performance results are measured using the SSVEP dataset. In this dataset, 11 subjects are used to perform experiments and 256-channel EEG data is taken. The efficiency of the proposed ASFSCE model is measured in terms of mean target detection accuracy and mean information transfer rate (ITR) in bits per minute. The average detection accuracy and ITR are evaluated by considering 23 trials for each subject. The obtained detection accuracy is 93.47% and ITR is 308.23 bpm.

A deep learning method for classification of steady-state visual evoked potentials in a brain-computer interface speller

ABSTRACT The main drawback of SSVEP-based BCIs is the lack of a classifier that categorizes SSVEPs with high accuracy and Information Transfer Rate (ITR). Addressing this, we proposed a deep convolutional neural network (CNN) for classifying a 40-class SSVEP. Time windows of length 2 and 3.5 seconds were used for training and testing the model by leave-one-subject-out cross-validation, using nine, three, and single-channel EEG. The proposed model reached 88.5% average accuracy for the nine-channel EEG with the mean and max ITR of 72 and 91.23 bpm, respectively. It outperformed the previous deep learning methods for SSVEP-based BCIs, in terms of accuracy and ITR. In the three-channel experiment the mean accuracy and ITR were 76.02% and 40.1 bpm. In single-channel implementation, O1 channel achieved 77.38 % average accuracy (highest) and the mean ITR was 57.51 bpm. The model showed promising performance to put this technology forward and make it more practical.

EEG-based hybrid QWERTY mental speller with high information transfer rate.

Brain-computer interface (BCI) spellers detect variations in brain waves to help subjects communicate with the world. This study introduces a P300-SSVEP hybrid BCI-based QWERTY speller. The proposed hybrid speller, combines SSVEP and P300 features using a hybrid paradigm. P300 was used as time division multiplexing index which results in the use of lesser number of assumed frequencies for SSVEP elicitation. Each flickering frequency was also assigned a unique colour, to enhance system accuracy. On the basis of 20 subjects, an average accuracy of classification of 96.42% and a mean information transfer rate (ITR) of 131.0 bits per min. (BPM) was achieved during the free spelling trial (trial-F). The t test results revealed that the hybrid QWERTY speller performed significantly better (on the basis of mean classification accuracy and ITR) as compared to the traditional P300 speller) and the QWERTY SSVEP speller. Also, the amount of time taken to spell a word was significantly lesser in the case of hybrid QWERTY speller in contrast to traditional P300 speller while it was almost the same as compared to QWERTY SSVEP speller. QWERTY speller outperformed the stereotypical P300 speller as well as QWERTY SSVEP speller.

Low-Cost Brain-Computer Interface Using the Emotiv Epoc Headset Based on Rotating Vanes

Detailed In the brain-computer interface system (BCI), electroencephalography (EEG) signals are converted into digital signals and analyzed, allowing direct communication between humans and the electronic devices around them. The convenience of the user and the speed of communication with the surrounding devices are the most important challenges of BCI systems. The Emotiv Epoc headset minimizes the discomfort of the user thanks to its wet electrodes and easy handling. In the continuation of our previous works, in this paper, we developed our BCI system based on the gaze at the rotating vanes using the inexpensive Emotiv Epoc headset. In addition to user comfort, our design has an acceptable mean accuracy rate (ACC) and mean information transfer rate (ITR) compared to similar systems.

Towards solving of the Illiteracy phenomenon for VEP-based brain-computer interfaces

Brain-Computer Interface (BCI) systems use brain activity as an input signal and enable communication without requiring bodily movement. This novel technology may help impaired patients and users with disabilities to communicate with their environment. Over the years, researchers investigated the performance of subjects in different BCI paradigms, stating that 15%–30% of BCI users are unable to reach proficiency in using a BCI system and therefore were labelled as BCI illiterates. Recent progress in the BCIs based on the visually evoked potentials (VEPs) necessitates re-considering of this term, as very often all subjects are able to use VEP-based BCI systems. This study examines correlations among BCI performance, personal preferences, and further demographic factors for three different modern visually evoked BCI paradigms: (1) the conventional Steady-State Visual Evoked Potentials (SSVEPs) based on visual stimuli flickering at specific constant frequencies (fVEP), (2) Steady-State motion Visual Evoked Potentials (SSmVEP), and (3) code-modulated Visual Evoked Potentials (cVEP). Demographic parameters, as well as handedness, vision correction, BCI experience, etc., have no significant effect on the performance of VEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only 20 out of a total of 86 participants indicated a change in fatigue during the experiment. 83 subjects were able to successfully finish all spelling tasks with the fVEP speller, with a mean (SD) information transfer rate of 31.87 bit/min (9.83) and an accuracy of 95.28% (5.18), respectively. Compared to that, 80 subjects were able to successfully finish all spelling tasks using SSmVEP, with a mean information transfer rate of 26.44 bit/min (8.04) and an accuracy of 91.10% (6.01), respectively. Finally, all 86 subjects were able to successfully finish all spelling tasks with the cVEP speller, with a mean information transfer rate of 40.23 bit/min (7.63) and an accuracy of 97.83% (3.37).

Towards enhanced information transfer rate: a comparative study based on classification techniques

ABSTRACT One of the most important performance parameters for Assistive Devices (AD’s) based on Brain Computer Interfaces (BCIs) is the Information Transfer Rate (ITR). This study compares a hybrid BCI to a Steady State Visually Evoked Potential (SSVEP) based BCI, along with a comparison of classification techniques used for translation of user intentions. The hybrid BCI paradigm combined SSVEP & P300, where SSVEP decodes user intentions and P300 is used for Time Division Multiplexing (TDM). The classification protocols were categorised as single-step supervised classifiers and two-step unsupervised classifier. It was observed that the classification accuracy for translation of human intentions for the traditional SSVEP paradigm (93.78%) was higher than the hybrid BCI (90.76%) proposed, but still the hybrid BCI is paradigm option for development of ADs (high ITR of 81.10 bits/minute). The study compared the two classification protocols using the statistical t-value test, which concluded that (99.9% confidence level) the mean classification accuracy and mean ITR were greater for the single-step supervised classification and also that the mean FAR was lower for the single-step supervised classification. The proposed hybrid BCI with single-step supervised learning classification protocol emerged as best BCI option for the development of AD’s.

Development of a High-speed Mental Spelling System Combining Eye Tracking and SSVEP-based BCI with High Scalability.

Hybrid brain-computer interfaces (BCIs) have been proved to be more effective in mental control. In this study, a hybrid BCI speller system combining steady-state visual evoked potentials (SSVEPs) and eye tracking has been proposed. In this system, the eye tracker was used to detect eye gaze position for a 3×3 block selection, after that classification of the command was achieved through filter bank canonical correlation analysis (FBCCA) method. Results showed that the 40-classes hybrid speller system outperformed the SSVEP-only method, achieved a mean accuracy of 92.1% and a mean information transfer rate (ITR) of 180.8 bits/min during online experiments, and the scalability of the proposed system also has been tested with larger number of commands.

A Novel Dictionary-Driven Mental Spelling Application Based on Code-Modulated Visual Evoked Potentials

Brain–computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs) typically utilize a synchronous approach to identify targets (i.e., after preset time periods the system produces command outputs). Hence, users have only a limited amount of time to fixate a desired target. This hinders the usage of more complex interfaces, as these require the BCI to distinguish between intentional and unintentional fixations. In this article, we investigate a dynamic sliding window mechanism as well as the implementation of software-based stimulus synchronization to enable the threshold-based target identification for the c-VEP paradigm. To further improve the usability of the system, an ensemble-based classification strategy was investigated. In addition, a software-based approach for stimulus on-set determination is proposed, which allows for an easier setup of the system, as it reduces additional hardware dependencies. The methods were tested with an eight-target spelling application utilizing an n-gram word prediction model. The performance of eighteen participants without disabilities was tested; all participants completed word- and sentence spelling tasks using the c-VEP BCI with a mean information transfer rate (ITR) of 75.7 and 57.8 bpm, respectively.

Eyelid Drive System: An Assistive Technology Employing Inductive Sensing of Eyelid Movement.

This paper presents the design, development, and validation of the eyelid drive system (EDS), an assistive technology comprising a specialized pair of glasses and millimeter-sized passive resonators, attached to the user's eyelids, that transduce eyelid movement (blinking and winking) through inductive sensing. The theory of operation and design optimization with simulations are presented. A proof-of-concept prototype EDS was constructed using a pair of nonprescription glasses and commercial-off-the-shelf components. In benchtop tests with model eyelids, the EDS demonstrated basic functionality. Initial trials were performed involving six human subjects interacting with custom designed graphical user interfaces on a computer. A group mean accuracy of 96.3% was achieved using a set of four different commands at a response rate of 3 s. A mean information transfer rate (ITR) of 56.1 b/min over all subjects was achieved with a set of six different commands at a response rate of 1.5 s. This proof-of-concept device consumes 51.6mW of power. The EDS compares favorably with related eye-interfacing assistive technologies and provides a unique combination of advantages, including high accuracy and ITR, wearability, insensitivity to lighting and noise conditions, obviation of facial electrodes, and the use of nonexaggerated gestures.

Detection of attention in multi-talker scenarios: A fuzzy approach

The automatic and online detection of auditory attention in multi-talker scenarios (e.g., cocktail party paradigm) is a current topic in electroencephalography (EEG)-based brain-computer interfaces (BCIs). Recent works have demonstrated a way to make it possible by means of a model based on an m-ary phase shift keying (m-PSK) detector. However, this attention detection model lacks of relevant information such as the non-stationary nature of EEG signals, the neuro-plasticity/habituation effects or the nonlinearities of the attention. In this paper we propose an enriched version of the attention detection model constituted by an automatic adaptive m-PSK detector implemented on fuzzy logic. In it, the relevant information mentioned before is modeled as two inputs that feed the fuzzy-based attention detection model. The output provides the detection. Our enriched model outperformed the results of previous works in terms of mean information transfer rate (ITR) (4-PSK: 5.41bpm; 6-PSK: 6.03bpm) and accuracy (4-PSK: 0.54; 6-PSK: 0.39) after only 4.63 (4-PSK) and 2.93 (6-PSK) seconds of processing. The proposed model for the automatic detection of auditory attention can have relevant impact on several areas such as education, public transport, jobs, industry, attention disorders, ubiquitous systems, sports and art.

A P300-Based Brain–Computer Interface for Chinese Character Input

ABSTRACTThe majority of previously developed assistive communication brain–computer interface systems have primarily focused on languages that are written in alphabetic scripts. However, languages that are written in logographic scripts, such as those in Chinese hanzi (or sinograms), pose a challenge for the implementation of visual spelling systems because it is impossible to simultaneously display thousands of items in a stimulus matrix of a reasonable size. In this study, a P300 visual spelling system that uses a novel method to input Chinese sinograms developed with a Hanyu Pinyin-based method is presented. This method transcribes a Chinese Pinyin into initial consonant and vowel components according to its Mandarin pronunciation. In this paradigm, each sinogram is input by selecting the initial consonant and then the vowel components and subsequently selecting the sinogram itself. Ten healthy subjects participated in the study and achieved an average offline accuracy of 92.6% with a mean information transfer rate of 39.2 bits/min and an average online input speed of one sinogram per 43.9 s. The preliminary results presented here indicated that the online input of Chinese text using a Pinyin-based visual speller is feasible.

An online three-class Transcranial Doppler ultrasound brain computer interface

Brain computer interfaces (BCI) can provide communication opportunities for individuals with severe motor disabilities. Transcranial Doppler ultrasound (TCD) measures cerebral blood flow velocities and can be used to develop a BCI. A previously implemented TCD BCI system used verbal and spatial tasks as control signals; however, the spatial task involved a visual cue that awkwardly diverted the user's attention away from the communication interface. Therefore, vision-independent right-lateralized tasks were investigated. Using a bilateral TCD BCI, ten participants controlled online, an on-screen keyboard using a left-lateralized task (verbal fluency), a right-lateralized task (fist motor imagery or 3D-shape tracing), and unconstrained rest. 3D-shape tracing was generally more discernible from other tasks than was fist motor imagery. Verbal fluency, 3D-shape tracing and unconstrained rest were distinguished from each other using a linear discriminant classifier, achieving a mean agreement of κ=0.43±0.17. These rates are comparable to the best offline three-class TCD BCI accuracies reported thus far. The online communication system achieved a mean information transfer rate (ITR) of 1.08±0.69bits/min with values reaching up to 2.46bits/min, thereby exceeding the ITR of previous online TCD BCIs. These findings demonstrate the potential of a three-class online TCD BCI that does not require visual task cues.

Brain–computer interfaces using capacitive measurement of visual or auditory steady-state responses

Objective. Brain–computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings. Approach. A capacitive electrode that does not require an electrolytic gel or direct electrode–scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed. Main results. Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively. Significance. The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.

Asynchronous BCI Based on Motor Imagery With Automated Calibration and Neurofeedback Training

A new multiclass brain-computer interface (BCI) based on the modulation of sensorimotor oscillations by imagining movements is described. By the application of advanced signal processing tools, statistics and machine learning, this BCI system offers: 1) asynchronous mode of operation, 2) automatic selection of user-dependent parameters based on an initial calibration, 3) incremental update of the classifier parameters from feedback data. The signal classification uses spatially filtered signals and is based on spectral power estimation computed in individualized frequency bands, which are automatically identified by a specially tailored AR-based model. Relevant features are chosen by a criterion based on Mutual Information. Final recognition of motor imagery is effectuated by a multinomial logistic regression classifier. This BCI system was evaluated in two studies. In the first study, five participants trained the ability to imagine movements of the right hand, left hand and feet in response to visual cues. The accuracy of the classifier was evaluated across four training sessions with feedback. The second study assessed the information transfer rate (ITR) of the BCI in an asynchronous application. The subjects' task was to navigate a cursor along a computer rendered 2-D maze. A peak information transfer rate of 8.0 bit/min was achieved. Five subjects performed with a mean ITR of 4.5 bit/min and an accuracy of 74.84%. These results demonstrate that the use of automated interfaces to reduce complexity for the intended operator (outside the laboratory) is indeed possible. The signal processing and classifier source code embedded in BCI2000 is available from https://www.brain-project.org/downloads.html.

BCI Demographics II: How Many (and What Kinds of) People Can Use a High-Frequency SSVEP BCI?

Brain-computer interface (BCI) systems use brain activity as an input signal and enable communication without movement. This study is a successor of our previous study (BCI demographics I) and examines correlations among BCI performance, personal preferences, and different subject factors such as age or gender for two sets of steady-state visual evoked potential (SSVEP) stimuli: one in the medium frequency range (13, 14, 15 and 16 Hz) and another in the high-frequency range (34, 36, 38, 40 Hz). High-frequency SSVEPs (above 30 Hz) diminish user fatigue and risk of photosensitive epileptic seizures. Results showed that most people, despite having no prior BCI experience, could use the SSVEP-based Bremen-BCI system in a very noisy field setting at a fair. Results showed that demographic parameters as well as handedness, tiredness, alcohol and caffeine consumption, etc., have no significant effect on the performance of SSVEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only five out of total 86 participants indicated change in fatigue during the experiment. 84 subjects performed with a mean information transfer rate of 17.24 ±6.99 bit/min and an accuracy of 92.26 ±7.82% with the medium frequency set, whereas only 56 subjects performed with a mean information transfer rate of 12.10 ±7.31 bit/min and accuracy of 89.16 ±9.29% with the high-frequency set. These and other demographic analyses may help identify the best BCI for each user.

Brain–computer interface using water-based electrodes

Current brain–computer interfaces (BCIs) that make use of EEG acquisition techniques require unpleasant electrode gel causing skin abrasion during the standard preparation procedure. Electrodes that require tap water instead of electrolytic electrode gel would make both daily setup and clean up much faster, easier and comfortable. This paper presents the results from ten subjects that controlled an SSVEP-based BCI speller system using two EEG sensor modalities: water-based and gel-based surface electrodes. Subjects performed in copy spelling mode using conventional gel-based electrodes and water-based electrodes with a mean information transfer rate (ITR) of 29.68 ± 14.088 bit min−1 and of 26.56 ± 9.224 bit min−1, respectively. A paired t-test failed to reveal significant differences in the information transfer rates and accuracies of using gel- or water-based electrodes for EEG acquisition. This promising result confirms the operational readiness of water-based electrodes for BCI applications.

A Dictionary-Driven P300 Speller With a Modified Interface

P300 spellers are mainly composed of an interface, by which alphanumerical characters are presented to users, and a classification system, which identifies the target character by using acquired EEG data. In this study, we proposed modifications both to the interface and to the classification system, in order to reduce the number of required stimulus repetitions and consequently boost the information transfer rate. We initially incorporated a custom-built dictionary into the classification system, and conducted a study on 14 healthy subjects who copy-spelled 15 four letter words. Incorporating the dictionary, the mean accuracy at five trials increased from 72.86% to 95.71%. To further increase the system performance, we first validated the hypothesis that for a conventional P300 system, most target-error pairs lie on the same row or column. Then based on the validated hypothesis, we adjusted letter positions on the well-known from A to Z interface. The same subjects spelled the same 15 words using the modified interface as well, and the mean information transfer rate at two trials reached 55.32 bits/min.

Model analyses of visual biofeedback training for EEG-based brain-computer interface

The primary goal of this study was to construct a simulation model of a biofeedback brain-computer interface (BCI) system to analyze the effect of biofeedback training on BCI users. A mathematical model of a man-machine visual-biofeedback BCI system was constructed to simulate a subject using a BCI system to control cursor movements. The model consisted of a visual tracking system, a thalamo-cortical model for EEG generation, and a BCI system. The BCI system in the model was realized for real experiments of visual biofeedback training. Ten sessions of visual biofeedback training were performed in eight normal subjects during a 3-week period. The task was to move a cursor horizontally across a screen, or to hold it at the screen's center. Experimental conditions and EEG data obtained from real experiments were then simulated with the model. Three model parameters, representing the adaptation rate of gain in the visual tracking system and the relative synaptic strength between the thalamic reticular and thalamo-cortical cells in the Rolandic areas, were estimated by optimization techniques so that the performance of the model best fitted the experimental results. The serial changes of these parameters over the ten sessions, reflecting the effects of biofeedback training, were analyzed. The model simulation could reproduce results similar to the experimental data. The group mean success rate and information transfer rate improved significantly after training (56.6 to 81.1% and 0.19 to 0.76 bits/trial, respectively). All three model parameters displayed similar and statistically significant increasing trends with time. Extensive simulation with systematic changes of these parameters also demonstrated that assigning larger values to the parameters improved the BCI performance. We constructed a model of a biofeedback BCI system that could simulate experimental data and the effect of training. The simulation results implied that the improvement was achieved through a quicker adaptation rate in visual tracking gain and a larger synaptic gain from the visual tracking system to the thalamic reticular cells. In addition to the purpose of this study, the constructed biofeedback BCI model can also be used both to investigate the effects of different biofeedback paradigms and to test, estimate, or predict the performances of other newly developed BCI signal processing algorithms.

Information transfer rate of nonspiking afferent neurons in the crab.

The thoracic-coxal muscle receptor organ (TCMRO) is the only proprioceptor at the thoracic-coxal joint in the crab leg. The S and T afferent neurons of the TCMRO convey signals to the CNS solely by means of graded changes in membrane potential. The rate of information transfer of these afferents was determined by measuring the signal-to-noise ratio (SNuR) of these cells after repeated stimulation of the receptor with identical sequences of random movement and applying the Shannon formula for the information capacity of a Gaussian channel. Intracellular recordings were made from the S and T afferents adjacent to the transduction site at the origin of the receptor and along the axon 5-7 mm distal to this site. These nonspiking afferents transduce receptor movement and transmit this information with extremely high fidelity. The SNR of both neurons near the transduction site was >1000 over most of the 200 Hz stimulation bandwidth, and the mean information transfer rate was approximately 2,500 bits/s. When calculated over a wider bandwidth of 500 Hz, the information rate was >4,600 bits/s. The effect of axonal cable properties on the information rate was evaluated by determining the SNR from membrane potential recordings made 5-7 mm distal to the transduction region. The major effect of graded transmission along the axon was attenuation and low-pass filtering of the sensory signal. The consequent reduction in signal power and bandwidth decreased the information transfer by approximately 10-15% over 200 Hz and approximately 30% over a 500 Hz bandwidth.