Does bimodal stimulus presentation increase ERP components usable in BCIs?

Thurlings, Marieke E.; Brouwer, A.; Erp, J.B.F. van; Blankertz, Benjamin; Werkhoven, Peter

doi:10.1088/1741-2560/9/4/045005

Cited by 38 publications

(44 citation statements)

References 54 publications

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“…To the best of our knowledge, only two other BCI-related studies investigated bimodal stimuli: visual-tactile stimuli (Brouwer et al, 2010), and audio-visual stimuli (Belitski et al, 2011). In both studies the authors reported increased classification accuracies (i.e., the percentage of correctly classified target responses) for bimodal compared to unimodal conditions, which is in line with the trend we reported in Thurlings et al (2012a). …”

Section: Introductionsupporting

confidence: 92%

“…BCI performance of tactile ERP-BCIs (Brouwer et al, 2010; Thurlings et al, 2012a,b) is generally lower than that of gaze-dependent BCIs (Thurlings et al, 2012a,b). In addition, when a BCI is used as a control device in the context of a dual-task, for example to navigate in a game, BCI performance is even lower than in BCI-only tasks (Thurlings et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, our first research question is: Are ERP components (quantified in the topographic Area Under the Curve or tAUC; Thurlings et al, 2012a,b) and corresponding classification accuracies of a bimodal visual-tactile ERP-BCI enhanced compared to its unimodal counterparts? We hypothesize enhanced bimodal ERP tAUCs both on early and late stages of processing when employing location-congruent bimodal stimuli in a gaze-independent setup, which should result in enhanced classification accuracies.…”

Section: Introductionmentioning

confidence: 99%

“…This is important for the designing of bimodal ERP-BCIs, especially since the most straightforward design might employ location incongruent bimodal stimulus pairs as in Thurlings et al (2012a). In that study, a display presented visual navigation information and included visual stimuli located at the possible navigation directions.…”

Section: Introductionmentioning

confidence: 99%

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Gaze-independent ERP-BCIs: augmenting performance through location-congruent bimodal stimuli

Thurlings

Brouwer²,

Erp³

et al. 2014

Front. Syst. Neurosci.

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Gaze-independent event-related potential (ERP) based brain-computer interfaces (BCIs) yield relatively low BCI performance and traditionally employ unimodal stimuli. Bimodal ERP-BCIs may increase BCI performance due to multisensory integration or summation in the brain. An additional advantage of bimodal BCIs may be that the user can choose which modality or modalities to attend to. We studied bimodal, visual-tactile, gaze-independent BCIs and investigated whether or not ERP components’ tAUCs and subsequent classification accuracies are increased for (1) bimodal vs. unimodal stimuli; (2) location-congruent vs. location-incongruent bimodal stimuli; and (3) attending to both modalities vs. to either one modality. We observed an enhanced bimodal (compared to unimodal) P300 tAUC, which appeared to be positively affected by location-congruency (p = 0.056) and resulted in higher classification accuracies. Attending either to one or to both modalities of the bimodal location-congruent stimuli resulted in differences between ERP components, but not in classification performance. We conclude that location-congruent bimodal stimuli improve ERP-BCIs, and offer the user the possibility to switch the attended modality without losing performance.

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Section: Introductionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gaze-independent ERP-BCIs: augmenting performance through location-congruent bimodal stimuli

Thurlings

Brouwer²,

Erp³

et al. 2014

Front. Syst. Neurosci.

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“…This type of classifier is known to perform well on a wide range of ERP-BCI paradigms including those with visual, auditory and tactile stimuli [37], [47]–[49]. It was trained on the data of 30 labelled trials collected during one block of calibration.…”

Section: Methodsmentioning

confidence: 99%

True Zero-Training Brain-Computer Interfacing – An Online Study

et al. 2014

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Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model.

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