P300-based Brain-computer Interface Research Articles

A hybrid brain-computer interface control strategy in a virtual environment

This paper presents a hybrid brain-computer interface (BCI) control strategy, the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment. The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to navigate in the virtual world. The two electroencephalography (EEG) patterns serve as source signals for different control functions in their corresponding system states, and state switch is achieved in a sequential manner. In the current system, imagination of left/right hand movement was translated into turning left/right in the virtual apartment continuously, while P300 potentials were mapped to discrete virtual device control commands using a five-oddball paradigm. The combination of motor imagery and P300 patterns in one BCI system for virtual environment control was tested and the results were compared with those of a single motor imagery or P300-based BCI. Subjects obtained similar performances in the hybrid and single control tasks, which indicates the hybrid control strategy works well in the virtual environment.

Workload measurement in a communication application operated through a P300-based brain–computer interface

Advancing the brain–computer interface (BCI) towards practical applications in technology-based assistive solutions for people with disabilities requires coping with problems of accessibility and usability to increase user acceptance and satisfaction. The main objective of this study was to introduce a usability-oriented approach in the assessment of BCI technology development by focusing on evaluation of the user's subjective workload and satisfaction. The secondary aim was to compare two applications for a P300-based BCI. Eight healthy subjects were asked to use an assistive technology solution which integrates the P300-based BCI with commercially available software under two conditions—visual stimuli needed to evoke the P300 response were either overlaid onto the application's graphical user interface or presented on a separate screen. The two conditions were compared for effectiveness (level of performance), efficiency (subjective workload measured by means of NASA-TXL) and satisfaction of the user. Although no significant difference in usability could be detected between the two conditions, the methodology proved to be an effective tool to highlight weaknesses in the technical solution.

Manipulating attention via mindfulness induction improves P300-based brain–computer interface performance

In this study, we examined the effects of a short mindfulness meditation induction (MMI) on the performance of a P300-based brain–computer interface (BCI) task. We expected that MMI would harness present-moment attentional resources, resulting in two positive consequences for P300-based BCI use. Specifically, we believed that MMI would facilitate increases in task accuracy and promote the production of robust P300 amplitudes. Sixteen-channel electroencephalographic data were recorded from 18 subjects using a row/column speller task paradigm. Nine subjects participated in a 6 min MMI and an additional nine subjects served as a control group. Subjects were presented with a 6 × 6 matrix of alphanumeric characters on a computer monitor. Stimuli were flashed at a stimulus onset asynchrony (SOA) of 125 ms. Calibration data were collected on 21 items without providing feedback. These data were used to derive a stepwise linear discriminate analysis classifier that was applied to an additional 14 items to evaluate accuracy. Offline performance analyses revealed that MMI subjects were significantly more accurate than control subjects. Likewise, MMI subjects produced significantly larger P300 amplitudes than control subjects at Cz and PO7. The discussion focuses on the potential attentional benefits of MMI for P300-based BCI performance.

P300-based brain–computer interface for environmental control: an asynchronous approach

Brain–computer interface (BCI) systems allow people with severe motor disabilities to communicate and interact with the external world. The P300 potential is one of the most used control signals for EEG-based BCIs. Classic P300-based BCIs work in a synchronous mode; the synchronous control assumes that the user is constantly attending to the stimulation, and the number of stimulation sequences is fixed a priori. This issue is an obstacle for the use of these systems in everyday life; users will be engaged in a continuous control state, their distractions will cause misclassification and the speed of selection will not take into account users' current psychophysical condition. An efficient BCI system should be able to understand the user's intentions from the ongoing EEG instead. Also, it has to refrain from making a selection when the user is engaged in a different activity and it should increase or decrease its speed of selection depending on the current user's state. We addressed these issues by introducing an asynchronous BCI and tested its capabilities for effective environmental monitoring, involving 11 volunteers in three recording sessions. Results show that this BCI system can increase the bit rate during control periods while the system is proved to be very efficient in avoiding false negatives when the users are engaged in other tasks.

Suppressing flashes of items surrounding targets during calibration of a P300-based brain–computer interface improves performance

Since the introduction of the P300 brain–computer interface (BCI) speller by Farwell and Donchin in 1988, the speed and accuracy of the system has been significantly improved. Larger electrode montages and various signal processing techniques are responsible for most of the improvement in performance. New presentation paradigms have also led to improvements in bit rate and accuracy (e.g. Townsend et al (2010 Clin. Neurophysiol. 121 1109–20)). In particular, the checkerboard paradigm for online P300 BCI-based spelling performs well, has started to document what makes for a successful paradigm, and is a good platform for further experimentation. The current paper further examines the checkerboard paradigm by suppressing items which surround the target from flashing during calibration (i.e. the suppression condition). In the online feedback mode the standard checkerboard paradigm is used with a stepwise linear discriminant classifier derived from the suppression condition and one classifier derived from the standard checkerboard condition, counter-balanced. The results of this research demonstrate that using suppression during calibration produces significantly more character selections/min ((6.46) time between selections included) than the standard checkerboard condition (5.55), and significantly fewer target flashes are needed per selection in the SUP condition (5.28) as compared to the RCP condition (6.17). Moreover, accuracy in the SUP and RCP conditions remained equivalent (∼90%). Mean theoretical bit rate was 53.62 bits/min in the suppression condition and 46.36 bits/min in the standard checkerboard condition (ns). Waveform morphology also showed significant differences in amplitude and latency.

A P300-Based Brain–Computer Interface: Effects of Interface Type and Screen Size

As a nonmuscular communication and control system for people with severe motor disabilities, brain–computer interface (BCI) has found several applications. Although a few empirical studies of BCI user performance do exist, little to no research has specifically evaluated the impact of contributing factors on user performance in the BCI applications. To that end, our within-subjects design compared the impact of two different types of interface (ABC interface vs. frequency-based interface) and three levels of screen size (computer monitor, global positioning system, and cell phone screen) of a P300-based BCI application, P300 Speller, on user performance (accuracy, information transfer rate, amplitude, and latency) and usage preference. Ten participants with neuromuscular disabilities such as amyotrophic lateral sclerosis and cerebral palsy and 10 nondisabled participants were asked to type six, 10-character phrases in the P300 Speller. The overall accuracy was 79.7% for the nondisabled participants and 28.7% for participants with motor disabilities. The results showed that interface type and screen size have significant effects on user performance and usage preference, with varying degree of impact to participants with and without motor disabilities. Specifically, participants typed significantly more accurately in frequency-based interface and computer monitor screen. The results of this study should provide invaluable insights to the future research of P300-based BCI applications.

Predictive Spelling With a P300-Based Brain–Computer Interface: Increasing the Rate of Communication

This study compared a conventional P300 speller brain–computer interface (BCI) to one used in conjunction with a predictive spelling program. Performance differences in accuracy, bit rate, selections per minute, and output characters per minute (OCM) were examined. An 8 × 9 matrix of letters, numbers, and other keyboard commands was used. Participants (N = 24) were required to correctly complete the same 58 character sentence (i.e., correcting for errors) using the predictive speller (PS) and the nonpredictive speller (NS), counterbalanced. The PS produced significantly higher OCMs than the NS. Time to complete the task in the PS condition was 12 min 43 s as compared to 20 min 20 sec in the NS condition. Despite the marked improvement in overall output, accuracy was significantly higher in the NS paradigm. P300 amplitudes were significantly larger in the NS than in the PS paradigm, which is attributed to increased workload and task demands. These results demonstrate the potential efficacy of predictive spelling in the context of BCI.

Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis

AbstractThe effective use of brain–computer interfaces (BCIs) in real-world environments depends on a satisfactory throughput. In a P300-based BCI, this can be attained by reducing the number of trials needed to detect the P300 signal. However, this task is hampered by the very low signal-to-noise-ratio (SNR) of P300 event related potentials. This paper proposes an efficient methodology that achieves high classification accuracy and high transfer rates for both disabled and able-bodied subjects in a standard P300-based speller system. The system was tested by three subjects with cerebral palsy (CP), two subjects with amyotrophic lateral sclerosis (ALS), and nineteen able-bodied subjects.The paper proposes the application of three statistical spatial filters. The first is a beamformer that maximizes the ratio of signal power and noise power (Max-SNR). The second is a beamformer based on the Fisher criterion (FC). The third approach cascades the FC beamformer with the Max-SNR beamformer satisfying simultaneously sub-optimally both criteria (C-FMS). The calibration process of the BCI system takes about 5min to collect data and a couple of minutes to obtain spatial filters and classification models.Online results showed that subjects with disabilities have achieved, on average, an accuracy and transfer rate only slightly lower than able-bodied subjects. Taking 23 of the 24 participants, the averaged results achieved a transfer rate of 4.33 symbols per minute with a 91.80% accuracy, corresponding to a bandwidth of 19.18 bits per minute. This study shows the feasibility of the proposed methodology and that effective communication rates are achievable.

The P300-based brain–computer interface (BCI): Effects of stimulus rate

Objective Brain-computer interface technology can restore communication and control to people who are severely paralyzed. We have developed a non-invasive BCI based on the P300 event-related potential that uses an 8 × 9 matrix of 72 items that flash in groups of 6. Stimulus presentation rate (i.e., flash rate) is one of several parameters that could affect the speed and accuracy of performance. We studied performance (i.e., accuracy and characters/min) on copy spelling as a function of flash rate. Methods In the first study of six BCI users, stimulus-on and stimulus-off times were equal and flash rate was 4, 8, 16, or 32 Hz. In the second study of five BCI users, flash rate was varied by changing either the stimulus-on or stimulus-off time. Results For all users, lower flash rates gave higher accuracy. The flash rate that gave the highest characters/min varied across users, ranging from 8 to 32 Hz. However, variations in stimulus-on and stimulus-off times did not themselves significantly affect accuracy. Providing feedback did not affect results in either study suggesting that offline analyses should readily generalize to online performance. However there do appear to be session-specific effects that can influence the generalizability of classifier results. Conclusions The results show that stimulus presentation (i.e., flash) rate affects the accuracy and speed of P300 BCI performance. Significance These results extend the range over which slower flash rates increase the amplitude of the P300. Considering also presentation time, the optimal rate differs among users, and thus should be set empirically for each user. Optimal flash rate might also vary with other parameters such as the number of items in the matrix.

Operation of a P300-based brain–computer interface by individuals with cervical spinal cord injury

Objective This study evaluates the efficacy of a P300-based brain–computer interface (BCI) with green/blue flicker matrices for individuals with cervical spinal cord injury (SCI). Methods Ten individuals with cervical SCI (age 26–53, all male) and 10 age- and sex-matched able-bodied controls (age 27–52, all male) with no prior BCI experience were asked to input hiragana (Japanese alphabet) characters using the P300 BCI with two distinct types of visual stimuli, white/gray and green/blue, in an 8×10 flicker matrix. Both online and offline performance were evaluated. Results The mean online accuracy of the SCI subjects was 88.0% for the white/gray and 90.7% for the green/blue flicker matrices. The accuracy of the control subjects was 77.3% and 86.0% for the white/gray and green/blue, respectively. There was a significant difference in online accuracy between the two types of flicker matrix. SCI subjects performed with greater accuracy than controls, but the main effect was not significant. Conclusions Individuals with cervical SCI successfully controlled the P300 BCI, and the green/blue flicker matrices were associated with significantly higher accuracy than the white/gray matrices. Significance The P300 BCI with the green/blue flicker matrices is effective for use not only in able-bodied subjects, but also in individuals with cervical SCI.

Comparison of Pre-Processing and Classification Techniques for Single-Trial and Multi-Trial P300-Based Brain Computer Interfaces

The P300 component of Event Related Brain Potentials (ERP) is commonly used in Brain Computer Interfaces (BCI) to translate the intentio ns of an individual into commands for external devices. The P300 response, however, resides in a s ignal environment of high background noise. Consequently, the main problem in developing a P300-based BCI lies in identifying the P300 response in the presence of this noise. Traditionally, atten uating the background activity of P300 data is done by averaging multiple trials of recorded signals. This method, though effective, suffers two drawbacks. First, collecting multiple trials of data is time c onsuming and delays the BCI response. Second, latency distortions may appear in the averaged result due t o variable time-locking of the P300 in the individu al trials. Problem statement: The use of single-trial P300 data overcomes both t hese shortcomings. However, single-trial data must be properly denoise d to allow for reliable BCI operation. Single-trial P300-based BCIs have been implemented using a variety of signal processing techniques and classification methodologies. However, comparing the accuracies of these systems to other multi-trial systems is likely to include the comparison of more than just the trial format (single-trial/multi-tri al) as the data quality and recording circumstances are li kely to be dissimilar. Approach: This issue was directly addressed by comparing the performance comparison of three different preprocessing agents and three classification methodologies on the same data set over both the single-trial and multi-trial settings. The P300 data set of BCI Competition II w as used to facilitate this comparison. Results: The LDA classifier exhibited the best performance in cl assifying unseen P300 spatiotemporal features in both the single-trial (74.19%) and multi-trial form at (100%). It is also very efficient in terms of computational and memory requirements. Conclusion: This study can serve as a general guide for practitioners developing single-trial and multi-tri al P300-based BCI systems, particularly for selecti ng appropriate pre-processing agents and classificatio n methodologies for inclusion. The possibilities fo r future study include the investigation of double-tr ial and triple-trial P300 system based on the LDA classifier. The time savings of such approaches wil l still be significant. It is very likely that such systems would benefit from accuracies higher than t he one obtained in this study for single-trial LDA (74.19%).

Classification effects of real and imaginary movement selective attention tasks on a P300-based brain–computer interface

Brain–computer interfaces (BCIs) rely on various electroencephalography methodologies that allow the user to convey their desired control to the machine. Common approaches include the use of event-related potentials (ERPs) such as the P300 and modulation of the beta and mu rhythms. All of these methods have their benefits and drawbacks. In this paper, three different selective attention tasks were tested in conjunction with a P300-based protocol (i.e. the standard counting of target stimuli as well as the conduction of real and imaginary movements in sync with the target stimuli). The three tasks were performed by a total of 10 participants, with the majority (7 out of 10) of the participants having never before participated in imaginary movement BCI experiments. Channels and methods used were optimized for the P300 ERP and no sensory-motor rhythms were explicitly used. The classifier used was a simple Fisher's linear discriminant. Results were encouraging, showing that on average the imaginary movement achieved a P300 versus No-P300 classification accuracy of 84.53%. In comparison, mental counting, the standard selective attention task used in previous studies, achieved 78.9% and real movement 90.3%. Furthermore, multiple trial classification results were recorded and compared, with real movement reaching 99.5% accuracy after four trials (12.8 s), imaginary movement reaching 99.5% accuracy after five trials (16 s) and counting reaching 98.2% accuracy after ten trials (32 s).

Documenting, modelling and exploiting P300 amplitude changes due to variable target delays in Donchin's speller

The P300 is an endogenous event-related potential (ERP) that is naturally elicited by rare and significant external stimuli. P300s are used increasingly frequently in brain–computer interfaces (BCIs) because the users of ERP-based BCIs need no special training. However, P300 waves are hard to detect and, therefore, multiple target stimulus presentations are needed before an interface can make a reliable decision. While significant improvements have been made in the detection of P300s, no particular attention has been paid to the variability in shape and timing of P300 waves in BCIs. In this paper we start filling this gap by documenting, modelling and exploiting a modulation in the amplitude of P300s related to the number of non-targets preceding a target in a Donchin speller. The basic idea in our approach is to use an appropriately weighted average of the responses produced by a classifier during multiple stimulus presentations, instead of the traditional plain average. This makes it possible to weigh more heavily events that are likely to be more informative, thereby increasing the accuracy of classification. The optimal weights are determined through a mathematical model that precisely estimates the accuracy of our speller as well as the expected performance improvement w.r.t. the traditional approach. Tests with two independent datasets show that our approach provides a marked statistically significant improvement in accuracy over the top-performing algorithm presented in the literature to date. The method and the theoretical models we propose are general and can easily be used in other P300-based BCIs with minimal changes.

Design and Implementation of a P300-Based Brain-Computer Interface for Controlling an Internet Browser

An electroencephalographic (EEG) brain-computer interface (BCI) internet browser was designed and evaluated with 10 healthy volunteers and three individuals with advanced amyotrophic lateral sclerosis (ALS), all of whom were given tasks to execute on the internet using the browser. Participants with ALS achieved an average accuracy of 73% and a subsequent information transfer rate (ITR) of 8.6 bits/min and healthy participants with no prior BCI experience over 90% accuracy and an ITR of 14.4 bits/min. We define additional criteria for unrestricted internet access for evaluation of the presented and future internet browsers, and we provide a review of the existing browsers in the literature. The P300-based browser provides unrestricted access and enables free web surfing for individuals with paralysis.

Time-locked and phase-locked features of P300 event-related potentials (ERPs) for brain–computer interface speller

The brain–computer interface P300 speller is aimed to help those patients unable to activate muscles to spell words by utilizing their brain activity. However, a problem associated with the use of this brain–computer interface paradigm is the generation mechanics of P300 related to responses to visual stimuli. Herein, we investigated the event-related potential (ERP) response for the P300-based brain–computer interface speller. A signal preprocessing method integrated coherent average, principal component analysis (PCA) and independent component analysis (ICA) to reduce the dimensions and noise in the raw data. The time–frequency analysis was based on wavelet and two characteristic parameters of event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) were computed to indicate the evoked response (time-locked) and phase reset (phase-locked) activity, respectively. Results demonstrated that the proposed method was valid for the time-locked and phase-locked feature extraction and both the evoked response and phase reset contributed to the genesis of the P300 signal. These electrophysiological responses characteristics of ERPs would be used for BCI P300 speller design and its signal processing strategies.

Evaluation of P300-Based Brain-Computer Interface in Real-World Contexts

Despite recent advances in brain-computer interface (BCI) development, system usability still remains a large oversight. The goal of this study was to investigate the usability of a P300-based BCI system, P300 Speller, by assessing how background noise and interface color contrast affect user performance and BCI usage preference. Fifteen able-bodied participants underwent a 2 (low and high interface color contrast) × 3 (low, medium, and high background noise level) within-subjects design experiment, in which participants were asked to type six 10-character phrases in the P300 Speller paradigm. The overall accuracy in the study was 80.2%. Participants showed higher accuracy, higher information transfer rate, bigger amplitude, and smaller latency in the high interface color contrast condition than in the low contrast condition. Participants had better performance in the noisy condition than in the quiet condition, but the background noise effects were not statistically significant in the present study. These results should give some insight to the real-world applicability of the current P300 Speller as a nonmuscular communication system, especially for individuals with severe neuromuscular disabilities.

Which Physiological Components are More Suitable for Visual ERP Based Brain–Computer Interface? A Preliminary MEG/EEG Study

We investigated which evoked response component occurring in the first 800 ms after stimulus presentation was most suitable to be used in a classical P300-based brain-computer interface speller protocol. Data was acquired from 275 Magnetoencephalographic sensors in two subjects and from 61 Electroencephalographic sensors in four. To better characterize the evoked physiological responses and minimize the effect of response overlap, a 1000 ms Inter Stimulus Interval was preferred to the short (<400 ms) trial length traditionally used in this class of BCIs. To investigate which scalp regions conveyed information suitable for BCI, a stepwise linear discriminant analysis classifier was used. The method iteratively analyzed each individual sensor and determined its performance indicators. These were then plotted on a 2-D topographic head map. Preliminary results for both EEG and MEG data suggest that components other than the P300 maximally represented in the occipital region, could be successfully used to improve classification accuracy and finally drive this class of BCIs.

A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue.

Most P300-based brain-computer interface (BCI) approaches use the visual modality for stimulation. For use with patients suffering from amyotrophic lateral sclerosis (ALS) this might not be the preferable choice because of sight deterioration. Moreover, using a modality different from the visual one minimizes interference with possible visual feedback. Therefore, a multi-class BCI paradigm is proposed that uses spatially distributed, auditory cues. Ten healthy subjects participated in an offline oddball task with the spatial location of the stimuli being a discriminating cue. Experiments were done in free field, with an individual speaker for each location. Different inter-stimulus intervals of 1000 ms, 300 ms and 175 ms were tested. With averaging over multiple repetitions, selection scores went over 90% for most conditions, i.e., in over 90% of the trials the correct location was selected. One subject reached a 100% correct score. Corresponding information transfer rates were high, up to an average score of 17.39 bits/minute for the 175 ms condition (best subject 25.20 bits/minute). When presenting the stimuli through a single speaker, thus effectively canceling the spatial properties of the cue, selection scores went down below 70% for most subjects. We conclude that the proposed spatial auditory paradigm is successful for healthy subjects and shows promising results that may lead to a fast BCI that solely relies on the auditory sense.

A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns

An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation - the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988). Using an 8x9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9-12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data. Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP. These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability. The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.

Effects of P300-Based BCI Use on Reported Presence in a Virtual Environment

Brain–computer interfaces (BCIs) are becoming more and more popular as an input device for virtual worlds and computer games. Depending on their function, a major drawback is the mental workload associated with their use and there is significant effort and training required to effectively control them. In this paper, we present two studies assessing how mental workload of a P300-based BCI affects participants' reported sense of presence in a virtual environment (VE). In the first study, we employ a BCI exploiting the P300 event-related potential (ERP) that allows control of over 200 items in a virtual apartment. In the second study, the BCI is replaced by a gaze-based selection method coupled with wand navigation. In both studies, overall performance is measured and individual presence scores are assessed by means of a short questionnaire. The results suggest that there is no immediate benefit for visualizing events in the VE triggered by the BCI and that no learning about the layout of the virtual space takes place. In order to alleviate this, we propose that future P300-based BCIs in VR are set up so as require users to make some inference about the virtual space so that they become aware of it, which is likely to lead to higher reported presence.