Using a motor imagery questionnaire to estimate the performance of a Brain–Computer Interface based on object oriented motor imagery

Vučković, Aleksandra; Osuagwu, B.A.

doi:10.1016/j.clinph.2013.02.016

Cited by 98 publications

(91 citation statements)

References 51 publications

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“…Most of these studies focus on inter-subject variability from a physiological [2][3][4][5][6], anatomical [7,8], or psychological [9,10] perspectives. Although precise distinction between user-related and system-related causes of performance variations may not be simple [11], these studies provide a better understanding of these causes.…”

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.

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“…There are a number of signal modalities that may be extracted from the EEG during either real or imagined movements, such as event related synchronization and desynchronization (ERD/ERS), event related potentials (ERP) and movement related cortical potentials (MRCP) [3]. Of these, we have shown the MRCP to be an ideal candidate for allowing very early detection prior to the task occurrence.…”

Section: Introductionmentioning

confidence: 98%

“…If there is only one type of movement [5], then MRCP is also likely to be present in the 'focus' phase. In the second paradigm, there are only two phases: focus and preparation/execution [3]. In this case, volunteers are typically asked to perform different types of movements, so a response to 'the focus' cue is a general preparation, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Movement Intention from Movement-Related Cortical Potentials with Different Paradigms

Aliakbaryhosseinabadi

Jiang

Vučković

et al. 2014

Biosystems &Amp; Biorobotics

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Abstract. In this study, we compared the effects of two imagery paradigms typically used within the field of brain computer interfaces on the detection of movement intention from scalp electroencephalography (EEG). This issue is important in the rehabilitation area because of its direct relation with appropriately timed neurofeedback. Subjects were asked to imagine hand or foot movements using either a random or a non-random cue. Templates were constructed individually for each subject. Movement intent was detected according to the correlation between the movement related cortical potentials (MRCP) of single trials with the initial part of the template. The large Laplacian filter was used to increase the signal to noise ratio (SNR). For the random cue, the true positive rate (TPR) of detection of movement intention was 63.5±5.9% for foot movement and the corresponding detection latency was 202.8±129.5 ms before movement onset. For the non-random cue, foot movement intention was detected with TPR of 75.3±5.5% and latency of 291±169.3 ms. These results demonstrate that cue type, random or non-random, has a significant effect on the performance of MRCPbased movement intention detection algorithms.

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“…Related to the internal context, several studies have addressed performance variation in sensorimotor rhythm (SMR)-based BCIs [16]. These studies focus on inter-subject variability from an anatomical [17], [18], physiological [19]- [23], or psychological [24], [25] perspective or, in a lesser extent, on a combination of them [26]. In contrast, the intra-subject performance variation is less often investigated.…”

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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Using a motor imagery questionnaire to estimate the performance of a Brain–Computer Interface based on object oriented motor imagery

Cited by 98 publications

References 51 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

Detection of Movement Intention from Movement-Related Cortical Potentials with Different Paradigms

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

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