The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study

Nijboer, Femke; Birbaumer, Niels; Kübler, Andrea

doi:10.3389/fnins.2010.00055

Cited by 192 publications

(182 citation statements)

References 80 publications

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“…In a longitudinal study, motivational factors were shown to be correlated with BCI performance on participants with amyotrophic lateral sclerosis (ALS) [14]. Other studies have focused on neurophysiological markers.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

Saeedi

Chavarriaga

Leeb

et al. 2016

IEEE Comput. Intell. Mag.

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Abstract. One of the challenges of using brain-computer interfaces (BCIs) over extended periods of time is the variation of the users' performance from one experimental day to another. The goal of the current study is to propose a performance estimator for an electroencephalography-based motor imagery BCI by assessing the reliability of a command (i.e., predicting a 'short' or 'long' command delivery time, CDT). Using a short time window (< 1.5 s, shorter than the delivery time) of the mental task execution and a linear discriminant analysis classifier, we could reliably differentiate between short and long CDT (Area under the sensitivity-specificity curve, AUC ≈ 0.8) for 9 healthy subjects. Moreover, we assessed the feasibility of providing online adaptive assistance using the performance estimator in a BCI game by comparing two conditions: (i) allowing a 'fixed timeout' to deliver each command or (ii) providing 'adaptive assistance' by giving more time if the performance estimator detects a long CDT. The results revealed that providing adaptive assistance increases the ratio of correct commands significantly (p < 0.01). Moreover, the task load index (measured via the NASA TLX questionnaire) shows a significantly higher user acceptance in case of providing adaptive assistance (p < 0.01). Furthermore, the results obtained in this study were used to simulate a robotic navigation scenario, which showed how adaptive assistance improved performance.

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Section: Introductionmentioning

confidence: 99%

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

Saeedi

Chavarriaga

Leeb

et al. 2016

IEEE Comput. Intell. Mag.

View full text Add to dashboard Cite

show abstract

“…In contrast, the intra-subject performance variation is less often investigated. Studies on this issue are dedicated to psychological aspects [27] or neurophysiological markers [21]- [23], [28]- [30]. In the latter case, efforts have been made for identifying neural correlates for performance variation extracted from EEG oscillations in different frequency bands and brain regions.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Stable Control of Motor-Imagery BCI by a Locked-In User Through Adaptive Assistance

Saeedi

Chavarriaga

Millán

2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-Performance variation is one of the main challenges that BCIs are confronted with, when being used over extended periods of time. Shared control techniques could partially cope with such a problem. In this paper, we propose a taxonomy of shared control approaches used for BCIs and we review some of the recent studies at the light of these approaches. We posit that the level of assistance provided to the BCI user should be adjusted in real time in order to enhance BCI reliability over time. This approach has not been extensively studied in the recent literature on BCIs.In addition, we investigate the effectiveness of providing online adaptive assistance in a motor-imagery BCI for a tetraplegic enduser with an incomplete locked-in syndrome in a longitudinal study lasting 11 months. First, we report a reliable estimation of the BCI performance (in terms of command delivery time) using only a window of 1 s in the beginning of trials (AUC≈ 0.8). Second, we demonstrate how adaptive shared control can exploit the output of the performance estimator to adjust online the level of assistance in a BCI game by regulating its speed. In particular, online adaptive assistance was superior to a fixed condition in terms of success rate (p < 0.01). Remarkably, the results exhibited a stable performance over several months without recalibration of the BCI classifier or the performance estimator.

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“…The P300 has been exploited in many ways to produce a number of functional applications [6,7]. Although the specific technology used for recording the brain activity is closely tied to the final performance of the classifier used, [8,9] demonstrated that training and motivation have a positive and visible impact on the shape and appearance of the P300. Experimental setups using EEG and the P300 have been widely used in the development of BCI [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Identifying Single Trial Event-Related Potentials in an Earphone-Based Auditory Brain-Computer Interface

et al. 2017

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As brain-computer interfaces (BCI) must provide reliable ways for end users to accomplish a specific task, methods to secure the best possible translation of the intention of the users are constantly being explored. In this paper, we propose and test a number of convolutional neural network (CNN) structures to identify and classify single-trial P300 in electroencephalogram (EEG) readings of an auditory BCI. The recorded data correspond to nine subjects in a series of experiment sessions in which auditory stimuli following the oddball paradigm were presented via earphones from six different virtual directions at time intervals of 200, 300, 400 and 500 ms. Using three different approaches for the pooling process, we report the average accuracy for 18 CNN structures. The results obtained for most of the CNN models show clear improvement over past studies in similar contexts, as well as over other commonly-used classifiers. We found that the models that consider data from the time and space domains and those that overlap in the pooling process usually offer better results regardless of the number of layers. Additionally, patterns of improvement with single-layered CNN models can be observed.

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The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study

Cited by 192 publications

References 80 publications

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability

Long-Term Stable Control of Motor-Imagery BCI by a Locked-In User Through Adaptive Assistance

Identifying Single Trial Event-Related Potentials in an Earphone-Based Auditory Brain-Computer Interface

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