287 research outputs found

    The cost of space independence in P300-BCI spellers.

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    Background: Though non-invasive EEG-based Brain Computer Interfaces (BCI) have been researched extensively over the last two decades, most designs require control of spatial attention and/or gaze on the part of the user. Methods: In healthy adults, we compared the offline performance of a space-independent P300-based BCI for spelling words using Rapid Serial Visual Presentation (RSVP), to the well-known space-dependent Matrix P300 speller. Results: EEG classifiability with the RSVP speller was as good as with the Matrix speller. While the Matrix speller’s performance was significantly reliant on early, gaze-dependent Visual Evoked Potentials (VEPs), the RSVP speller depended only on the space-independent P300b. However, there was a cost to true spatial independence: the RSVP speller was less efficient in terms of spelling speed. Conclusions: The advantage of space independence in the RSVP speller was concomitant with a marked reduction in spelling efficiency. Nevertheless, with key improvements to the RSVP design, truly space-independent BCIs could approach efficiencies on par with the Matrix speller. With sufficiently high letter spelling rates fused with predictive language modelling, they would be viable for potential applications with patients unable to direct overt visual gaze or covert attentional focus

    Electroencephalogram Signal Processing For Hybrid Brain Computer Interface Systems

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    The goal of this research was to evaluate and compare three types of brain computer interface (BCI) systems, P300, steady state visually evoked potentials (SSVEP) and Hybrid as virtual spelling paradigms. Hybrid BCI is an innovative approach to combine the P300 and SSVEP. However, it is challenging to process the resulting hybrid signals to extract both information simultaneously and effectively. The major step executed toward the advancement to modern BCI system was to move the BCI techniques from traditional LED system to electronic LCD monitor. Such a transition allows not only to develop the graphics of interest but also to generate objects flickering at different frequencies. There were pilot experiments performed for designing and tuning the parameters of the spelling paradigms including peak detection for different range of frequencies of SSVEP BCI, placement of objects on LCD monitor, design of the spelling keyboard, and window time for the SSVEP peak detection processing. All the experiments were devised to evaluate the performance in terms of the spelling accuracy, region error, and adjacency error among all of the paradigms: P300, SSVEP and Hybrid. Due to the different nature of P300 and SSVEP, designing a hybrid P300-SSVEP signal processing scheme demands significant amount of research work in this area. Eventually, two critical questions in hybrid BCl are: (1) which signal processing strategy can best measure the user\u27s intent and (2) what a suitable paradigm is to fuse these two techniques in a simple but effective way. In order to answer these questions, this project focused mainly on developing signal processing and classification technique for hybrid BCI. Hybrid BCI was implemented by extracting the specific information from brain signals, selecting optimum features which contain maximum discrimination information about the speller characters of our interest and by efficiently classifying the hybrid signals. The designed spellers were developed with the aim to improve quality of life of patients with disability by utilizing visually controlled BCI paradigms. The paradigms consist of electrodes to record electroencephalogram signal (EEG) during stimulation, a software to analyze the collected data, and a computing device where the subject’s EEG is the input to estimate the spelled character. Signal processing phase included preliminary tasks as preprocessing, feature extraction, and feature selection. Captured EEG data are usually a superposition of the signals of interest with other unwanted signals from muscles, and from non-biological artifacts. The accuracy of each trial and average accuracy for subjects were computed. Overall, the average accuracy of the P300 and SSVEP spelling paradigm was 84% and 68.5 %. P300 spelling paradigms have better accuracy than both the SSVEP and hybrid paradigm. Hybrid paradigm has the average accuracy of 79 %. However, hybrid system is faster in time and more soothing to look than other paradigms. This work is significant because it has great potential for improving the BCI research in design and application of clinically suitable speller paradigm

    Emotional Brain-Computer Interfaces

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    Research in Brain-computer interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from adapting their operation to the emotional state of the user. BCIs have the advantage of having access to brain activity which can provide signicant insight into the user's emotional state. This information can be utilized in two manners. 1) Knowledge of the inuence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is still correctly interpreted in spite of signal deviations induced by the subject's emotional state. 2) The ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation. Thus, controlling a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates.\ud These two approaches of emotion utilization in BCI are elaborated in detail in this paper in the framework of noninvasive EEG based BCIs

    Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

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    A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance

    Practical Brain Computer Interfacing

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    A brain-computer interface (BCI) is a communication system that enables users to voluntary send messages or commands without movement. The classical goal of BCI research is to support communication and control for users with impaired communication due to illness or injury. Typical BCI applications are the operation of computer cursors, spelling programs or external devices, such as wheelchairs, robots and neural prostheses. The user sends modulated information to the BCI by engaging in mental tasks that produce distinct brain patterns. The BCI acquires signals from the user's brain and translates them into suitable communication. This dissertation aims to develop faster and more reliable non-invasive BCI communication based on the study of users learning process and their interaction with the BCI transducer. To date, BCI research has focused on the development of advanced pattern recognition and classification algorithms to improve accuracy and reliability of the classified patterns. However, even with optimal detection methods, successful BCI operation depends on the degree to which the users can voluntary modulate their brain signals. Therefore, learning to operate a BCI requires repeated practice with feedback that engages learning mechanisms in the brain. In this work, several aspects including signal processing techniques, feedback methods, experimental and training protocols, demographics, and applications were explored and investigated. Research was focused on two BCI paradigms, steady-state visual evoked potentials (SSVEP) and event-related (de-)synchronization (ERD/ERS). Signal processing algorithms for the detection of both brain patterns were applied and evaluated. A general application interface for BCI feedback tasks was developed to evaluate the practicability, reliability and acceptance of new feedback methods. The role of feedback and training was fully investigated on studies conducted with healthy subjects. The influence of demographics on BCIs was explored in two field studies with a large number of subjects. Results were supported through advanced statistical analysis. Furthermore, the BCI control was evaluated in a spelling application and a service robotic application. This dissertation demonstrates that BCIs can provide effective communication for most subjects. Presented results showed that improvements in the BCI transducer, training protocols, and feedback methods constituted the basis to achieve faster and more reliable BCI communication. Nevertheless, expert assistance is necessary for both initial configuration and daily operation, which reduces the practicability of BCIs for people who really need them

    Bacteria Hunt: A multimodal, multiparadigm BCI game

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    Brain-Computer Interfaces (BCIs) allow users to control applications by brain activity. Among their possible applications for non-disabled people, games are promising candidates. BCIs can enrich game play by the mental and affective state information they contain. During the eNTERFACE’09 workshop we developed the Bacteria Hunt game which can be played by keyboard and BCI, using SSVEP and relative alpha power. We conducted experiments in order to investigate what difference positive vs. negative neurofeedback would have on subjects’ relaxation states and how well the different BCI paradigms can be used together. We observed no significant difference in mean alpha band power, thus relaxation, and in user experience between the games applying positive and negative feedback. We also found that alpha power before SSVEP stimulation was significantly higher than alpha power during SSVEP stimulation indicating that there is some interference between the two BCI paradigms

    A MUSIC-based method for SSVEP signal processing

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    The research on brain computer interfaces (BCIs) has become a hotspot in recent years because it offers benefit to disabled people to communicate with the outside world. Steady state visual evoked potential (SSVEP)-based BCIs are more widely used because of higher signal to noise ratio and greater information transfer rate compared with other BCI techniques. In this paper, a multiple signal classification based method was proposed for multi-dimensional SSVEP feature extraction. 2-second data epochs from four electrodes achieved excellent accuracy rates including idle state detection. In some asynchronous mode experiments, the recognition accuracy reached up to 100 %. The experimental results showed that the proposed method attained good frequency resolution. In most situations, the recognition accuracy was higher than canonical correlation analysis, which is a typical method for multi-channel SSVEP signal processing. Also, a virtual keyboard was successfully controlled by different subjects in an unshielded environment, which proved the feasibility of the proposed method for multi-dimensional SSVEP signal processing in practical applications

    P300, Steady State Visual Evoked Potentials, And Hybrid Paradigms For A Brain Computer Interface Speller

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    The goal of this research was to evaluate and compare two types of brain computer interface (BCI) systems, P300 and steady state visually evoked potentials (SSVEP), as spelling paradigms and combine them as a hybrid approach. There were pilot experiments performed for designing the parameters of the SSVEP spelling paradigm including peak detection for different range of frequencies, placement of LEDs, design of the SSVEP stimulus board, and window time for the SSVEP peak detection processing. The next experiment was to evaluate the SSVEP spelling paradigm. Six subjects participated in the task. The accuracy of each frequency and average accuracy for each subject were considered. The second experiment was designed to compare the performance and accuracy of SSVEP, P300, and the combination of both paradigms as a simultaneous task. Ten subjects were considered for performing this experiment. Overall the average accuracy of the SSVEP spelling paradigm was 80.00 % and higher than the P300 spelling paradigm average accuracy which was 72.50 %, and both of the spelling paradigms have better accuracy than the hybrid paradigm with the average accuracy of 64.39 %

    Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

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    Brain-computer interface (BCI) is an emerging field, and an increasing number of BCI research projects are being carried globally to interface computer with human using EEG for useful operations in both healthy and locked persons. Although several methods have been used to enhance the BCI performance in terms of signal processing, noise reduction, accuracy, information transfer rate, and user acceptability, the effective BCI system is still in the verge of development. So far, various modifications on single BCI systems as well as hybrid are done and the hybrid BCIs have shown increased but insufficient performance. Therefore, more efficient hybrid BCI models are still under the investigation by different research groups. In this review chapter, single BCI systems are briefly discussed and more detail discussions on hybrid BCIs, their modifications, operations, and performances with comparisons in terms of signal processing approaches, applications, limitations, and future scopes are presented
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