Improving motor imagery BCI with user response to feedback

Mousavi, Mahta; Koerner, Adam S.; Zhang, Qiong; Noh, Eunho; de, Virginia R.

doi:10.1080/2326263x.2017.1303253

Cited by 27 publications

(30 citation statements)

References 20 publications

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“…This analysis will be done on the second and third trials of the third session, in which the user had feedback. Various authors such as [ 48 ] consider the first trial to be a training phase to give participants the opportunity to learn how to carry out the motor imagery, concentrating their analysis on the following phases. Moreover, the performance on the set of the 40 sequences of the second trial will be studied and we will also proceed with the 40 sequences of the third trial.…”

Section: Resultsmentioning

confidence: 99%

On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems

Riquelme-Ros

Rodríguez-Bermúdez

Rodríguez‐Rodríguez

et al. 2020

Sensors

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Motor imagery (MI)-based brain-computer interface (BCI) systems detect electrical brain activity patterns through electroencephalogram (EEG) signals to forecast user intention while performing movement imagination tasks. As the microscopic details of individuals’ brains are directly shaped by their rich experiences, musicians can develop certain neurological characteristics, such as improved brain plasticity, following extensive musical training. Specifically, the advanced bimanual motor coordination that pianists exhibit means that they may interact more effectively with BCI systems than their non-musically trained counterparts; this could lead to personalized BCI strategies according to the users’ previously detected skills. This work assessed the performance of pianists as they interacted with an MI-based BCI system and compared it with that of a control group. The Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) machine learning algorithms were applied to the EEG signals for feature extraction and classification, respectively. The results revealed that the pianists achieved a higher level of BCI control by means of MI during the final trial (74.69%) compared to the control group (63.13%). The outcome indicates that musical training could enhance the performance of individuals using BCI systems.

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Section: Resultsmentioning

confidence: 99%

On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems

Riquelme-Ros

Rodríguez-Bermúdez

Rodríguez‐Rodríguez

et al. 2020

Sensors

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“…Our analysis was restricted to time domain signals from specific channel groups known to be involved in frontal and parietal old/new effects. It is possible that accuracy could be increased by using frequency domain information from multiple electrode location and frequency bands (see for example, Hammon and de Sa, 2007 ; Hammon et al, 2008 ; Velu and de Sa, 2013 ; Noh et al, 2014a ; Mousavi et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent Differences

Noh

Liao

Mollison

et al. 2018

Front. Hum. Neurosci.

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We used pattern classifiers to extract features related to recognition memory retrieval from the temporal information in single-trial electroencephalography (EEG) data during attempted memory retrieval. Two-class classification was conducted on correctly remembered trials with accurate context (or source) judgments vs. correctly rejected trials. The average accuracy for datasets recorded in a single session was 61% while the average accuracy for datasets recorded in two separate sessions was 56%. To further understand the basis of the classifier’s performance, two other pattern classifiers were trained on different pairs of behavioral conditions. The first of these was designed to use information related to remembering the item and the second to use information related to remembering the contextual information (or source) about the item. Mollison and Curran (2012) had earlier shown that subjects’ familiarity judgments contributed to improved memory of spatial contextual information but not of extrinsic associated color information. These behavioral results were similarly reflected in the event-related potential (ERP) known as the FN400 (an early frontal effect relating to familiarity) which revealed differences between correct and incorrect context memories in the spatial but not color conditions. In our analyses we show that a classifier designed to distinguish between correct and incorrect context memories, more strongly involves early activity (400–500 ms) over the frontal channels for the location distinctions, than for the extrinsic color associations. In contrast, the classifier designed to classify memory for the item (without memory for the context), had more frontal channel involvement for the color associated experiments than for the spatial experiments. Taken together these results argue that location may be bound more tightly with the item than an extrinsic color association. The multivariate classification approach also showed that trial-by-trial variation in EEG corresponding to these ERP components were predictive of subjects’ behavioral responses. Additionally, the multivariate classification approach enabled analysis of error conditions that did not have sufficient trials for standard ERP analyses. These results suggested that false alarms were primarily attributable to item memory (as opposed to memory of associated context), as commonly predicted, but with little previous corroborating EEG evidence.

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“…This BCI output is referred to as BCI feedback and the brain response to BCI feedback as feedback-related brain activity. One source of non-stationarity in EEG signals is the feedback-related brain activity [ 5 ]. Error-related potentials (ErrP) and error-related spectral components are among the components of this signal [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…One source of non-stationarity in EEG signals is the feedback-related brain activity [ 5 ]. Error-related potentials (ErrP) and error-related spectral components are among the components of this signal [ 5 ]. If not taken into account, these signals can pose challenges for real-world application of a BCI system [ 5 – 7 ].…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal analysis of error-related brain activity in active and passive brain–computer interfaces

Mousavi

2019

Brain-Computer Interfaces

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Electroencephalography (EEG)-based brain–computer interface (BCI) systems infer brain signals recorded via EEG without using common neuromuscular pathways. User brain response to BCI error is a contributor to non-stationarity of the EEG signal and poses challenges in developing reliable active BCI control. Many passive BCI implementations, on the other hand, have the detection of error-related brain activity as their primary goal. Therefore, reliable detection of this signal is crucial in both active and passive BCIs. In this work, we propose CREST: a novel covariance-based method that uses Riemannian and Euclidean geometry and combines spatial and temporal aspects of the feedback-related brain activity in response to BCI error. We evaluate our proposed method with two datasets: an active BCI for 1-D cursor control using motor imagery and a passive BCI for 2-D cursor control. We show significant improvement across participants in both datasets compared to existing methods.

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Improving motor imagery BCI with user response to feedback

Cited by 27 publications

References 20 publications

On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems

On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems

Single-Trial EEG Analysis Predicts Memory Retrieval and Reveals Source-Dependent Differences

Spatio-temporal analysis of error-related brain activity in active and passive brain–computer interfaces

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