Performance Optimization of ERP-Based BCIs Using Dynamic Stopping

BBCI group of the Machine Learning Department, Berlin Institute of Technology, Berlin, Germany.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:4580-3. DOI: 10.1109/IEMBS.2011.6091134
Source: PubMed


Brain-computer interfaces based on event-related potentials face a trade-off between the speed and accuracy of the system, as both depend on the number of iterations. Increasing the number of iterations leads to a higher accuracy but reduces the speed of the system. This trade-off is generally dealt with by finding a fixed number of iterations that give a good result on the calibration data. We show here that this method is sub optimal and increases the performance significantly in only one out of five datasets. Several alternative methods have been described in literature, and we test the generalization of four of them. One method, called rank diff, significantly increased the performance over all datasets. These findings are important, as they show that 1) one should be cautious when reporting the potential performance of a BCI based on post-hoc offline performance curves and 2) simple methods are available that do boost performance.

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Available from: Johannes Höhne, Jul 03, 2014
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    • "However, static data collection, risks over or under collection of data since it does not assess the quality of the data that are measured. Adaptive data collection strategies have been proposed in previous research to balance P300 speller accuracy and character selection time for improved online communication rate [21] [22]. Some approaches optimize the amount of fixed data collection prior to each P300 speller session by maximizing the written symbol rate metric [10], the number of selections a user can correctly make in a minute taking into account error correction e.g. "
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    ABSTRACT: Objective: The P300 speller is a brain-computer interface (BCI) that can possibly restore communication abilities to individuals with severe neuromuscular disabilities, such as amyotrophic lateral sclerosis (ALS), by exploiting elicited brain signals in electroencephalography (EEG) data. However, accurate spelling with BCIs is slow due to the need to average data over multiple trials to increase the signal-to-noise ratio (SNR) of the elicited brain signals. Probabilistic approaches to dynamically control data collection have shown improved performance in non-disabled populations; however, validation of these approaches in a target BCI user population has not occurred. Approach: We have developed a data-driven algorithm for the P300 speller based on Bayesian inference that improves spelling time by adaptively selecting the number of trials based on the acute SNR of a user's EEG data. We further enhanced the algorithm by incorporating information about the user's language. In this current study, we test and validate the algorithms online in a target BCI user population, by comparing the performance of the dynamic stopping (DS) (or early stopping) algorithms against the current state-of-the-art method, static data collection, where the amount of data collected is fixed prior to online operation. Main results: Results from online testing of the DS algorithms in participants with ALS demonstrate a significant increase in communication rate as measured in bits/min (100-300%), and theoretical bit rate (100-550%), while maintaining selection accuracy. Participants also overwhelmingly preferred the DS algorithms. Significance: We have developed a viable BCI algorithm that has been tested in a target BCI population which has the potential for translation to improve BCI speller performance towards more practical use for communication.
    Journal of Neural Engineering 01/2015; 12(1):016013. DOI:10.1088/1741-2560/12/1/016013 · 3.30 Impact Factor
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    • "Navigation tasks can be regarded as more complex, as users individually decide on the path they take and as processing of their environment may distract them. (2) Recently, researchers reported great benefit of dynamic stopping methods for visual and auditory BCIs (e.g., [56-60]; for comparison of techniques [61,62]). The proposed algorithms stop the stimulation cycle when classification reached sufficient probability for identification of the intended target from the event-related EEG. "
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    ABSTRACT: People with severe disabilities, e.g. due to neurodegenerative disease, depend on technology that allows for accurate wheelchair control. For those who cannot operate a wheelchair with a joystick, brain-computer interfaces (BCI) may offer a valuable option. Technology depending on visual or auditory input may not be feasible as these modalities are dedicated to processing of environmental stimuli (e.g. recognition of obstacles, ambient noise). Herein we thus validated the feasibility of a BCI based on tactually-evoked event-related potentials (ERP) for wheelchair control. Furthermore, we investigated use of a dynamic stopping method to improve speed of the tactile BCI system. Positions of four tactile stimulators represented navigation directions (left thigh: move left; right thigh: move right; abdomen: move forward; lower neck: move backward) and N = 15 participants delivered navigation commands by focusing their attention on the desired tactile stimulus in an oddball-paradigm. Participants navigated a virtual wheelchair through a building and eleven participants successfully completed the task of reaching 4 checkpoints in the building. The virtual wheelchair was equipped with simulated shared-control sensors (collision avoidance), yet these sensors were rarely needed. We conclude that most participants achieved tactile ERP-BCI control sufficient to reliably operate a wheelchair and dynamic stopping was of high value for tactile ERP classification. Finally, this paper discusses feasibility of tactile ERPs for BCI based wheelchair control.
    Journal of NeuroEngineering and Rehabilitation 01/2014; 11(1):7. DOI:10.1186/1743-0003-11-7 · 2.74 Impact Factor
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    • "In addition, the EEG could be cleaned prior to computing classifier weights to avoid building classifiers based on artifacts. Furthermore, classification based on a dynamically adjusting number of trials may compensate small artifact contamination such that more trials can be presented if artifacts lower classification certainty (e.g., Lenhardt et al., 2008; Höhne et al., 2010; Liu et al., 2010; Jin et al., 2011; Schreuder et al., 2011a; for review, Schreuder et al., 2011b, 2013). "
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    ABSTRACT: This paper describes a case study with a patient in the classic locked-in state, who currently has no means of independent communication. Following a user-centered approach, we investigated event-related potentials elicited in different modalities for use in brain-computer interface systems. Such systems could provide her with an alternative communication channel. To investigate the most viable modality for achieving BCI based communication, classic oddball paradigms (1 rare and 1 frequent stimulus, ratio 1:5) in the visual, auditory and tactile modality were conducted (2 runs per modality). Classifiers were built on one run and tested offline on another run (and vice versa). In these paradigms, the tactile modality was clearly superior to other modalities, displaying high offline accuracy even when classification was performed on single trials only. Consequently, we tested the tactile paradigm online and the patient successfully selected targets without any error. Furthermore, we investigated use of the visual or tactile modality for different BCI systems with more than two selection options. In the visual modality, several BCI paradigms were tested offline. Neither matrix-based nor so-called gaze-independent paradigms constituted a means of control. These results may thus question the gaze-independence of current gaze-independent approaches to BCI. A tactile four-choice BCI resulted in high offline classification accuracies. Yet, online use raised various issues. Although performance was clearly above chance, practical daily life use appeared unlikely when compared to other communication approaches (e.g. partner scanning). Our results emphasize the need for user-centered design in BCI development including identification of the best stimulus modality for a particular user. Finally, the paper discusses feasibility of EEG-based BCI systems for patients in classic locked-in state and compares BCI to other AT solutions that we also tested during the study.
    Frontiers in Neuroscience 07/2013; 7:129. DOI:10.3389/fnins.2013.00129 · 3.66 Impact Factor
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