High Theta and Low Alpha Powers May Be Indicative of BCI-Illiteracy in Motor Imagery

Ahn, Minkyu; H, Cho; Ahn, Sangtae; Jun, Sung Chan

doi:10.1371/journal.pone.0080886

Cited by 159 publications

(168 citation statements)

References 44 publications

(54 reference statements)

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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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“…Three major MI studies on 193 subjects [26], 80 subjects [30] and 52 subjects [31], have specifically investigated this problem. These studies reported that between ~40% and ~60% of the subjects investigated could achieve an average accuracy in BCI control of only ~50% in the classic two-class scenario [32].…”

mentioning

confidence: 99%

Performance of Brain–Computer Interfacing Based on Tactile Selective Sensation and Motor Imagery

Yao

Sheng

Mrachacz-Kersting

et al. 2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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-A large proportion of users do not achieve adequate control using current non-invasive Brain-computer Interfaces (BCI). This issue has being coined "BCI-Illiteracy", and is observed among BCI modalities. Here, we compare the performance and BCI-illiteracy rate of tactile selective sensation (SS) and motor imagery (MI) BCI, for large subject samples. We analyzed 80 experimental sessions from 57 subjects with two-class SS protocols. For SS, the group average performance was 79.8±10.6%, with 43 out of the 57 subjects (75.4%) exceeding the 70% BCI-illiteracy threshold for left and right hand SS discrimination. When compared to previous results, this tactile BCI outperformed all other tactile BCIs currently available. We also analyzed 63 experiment sessions from 43 subjects with two-class MI BCI protocols, where the group average performance was 77.2±13.3%, with 69.7% of the subjects exceeded the 70% performance threshold for left and right hand MI. For within-subject comparison, the 24 subjects who participated to both the SS and MI experiments, the BCI performance was superior with SS than MI especially in beta frequency band (p<0.05), with enhanced R 2 discriminative information in the somatosensory cortex for the SS modality. Both SS and MI showed a functional dissociation between lower alpha ([8 10] Hz) and upper alpha ([10 13] Hz) bands, with BCI performance significantly better in the upper alpha than the lower alpha (p<0.05) band. In summary, we demonstrated that SS is a promising BCI modality with low BCI illiteracy issue, and has great potential in practical applications reaching large population.

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“…Furthermore, it has been hypothesized that gamma oscillations directly affect the sensorimotor rhythm and are responsible for performance variations in BCI ; this insight may be potentially used to help BCI-illiterate subjects to learn how to control a BCI system. Other recent work has studied the impact of the delta and theta rhythms on BCI performance (Vuckovic and Sepulveda, 2008b;Ahn et al, 2013).…”

Section: Neurophysiological Backgroundmentioning

confidence: 99%

On robust spatial filtering of EEG in nonstationary environments

Samek

2016

It - Information Technology

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bibliography 119 I N T R O D U C T I O NVariability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions which we know as disease. (Sir William Osler, 1903) T he Canadian physician Sir William Osler (1849 -1919 revolutionized the medical world of the 19th century by advocating an approach to therapy which focuses on the needs of individual patients. He recognized the great variability among individuals with respect to their physical conditions, their mental status and their responses to drugs and concluded that focusing solely on clinical signs and symptoms does not result in the most effective therapy. The development of this novel patient-centered paradigm made him the father of personalized medicine; a branch of medicine that is also very popular today due to a better understanding of the genetic influences on a person's risk of acquiring certain diseases.In the last two decades personalized approaches have also revolutionized the field of Brain-Computer Interfacing (BCI). A BCI system (see e.g. Wolpaw et al., 2002;Dornhege et al., 2007;Wolpaw and Wolpaw, 2012) aims to decode the intention of a user from recordings of brain activity and to use this information for controlling a computer application or a robotic device. Today's BCIs extract user-specific features from electroencelographic (EEG) recordings and adapt to the user's signal characteristics. This subject-centered or machine learning approach not only largely reduces the calibration time in comparison to classical BCI systems based on neurofeedback training (Kamiya et al., 1969) but also increases classification accuracy. Unfortunately, EEG responses to a stimulus or a task not only differ from subject to subject but also from trial to trial and from day to day. This change in the signal properties over time is termed the intrinsic nonstationarity of EEG (Kaplan et al., 2005) and constitutes a major challenge for data analysis and classification by violating a basic assumption of many machine learning methods, namely that data are sampled from a fixed (but unknown) distribution (Vapnik, 1998;Hastie et al., 2001). The analysis of EEG is further aggravated by the presence of artifacts in the data, e.g., eye movements, muscular activity or loose electrodes. The lack of robustness to unexpected events and the nonstationary nature of EEG are major reasons in explaining why current BCI technology is seldom used in out-of-lab scenarios and clinical practice. Since artifacts and nonstationarity can not be fully eliminated, even with the best experimental protocol, robust and invariant feature 1 2 introduction representations are required for optimal signal analysis and high classification accuracy. structure of the thesisThis thesis is divided into three parts. The first part introduces the basic concepts of motor imagery-based Brain-Computer Interfacing and discusses the advantages and limitations of several state-of-the-art spatial filtering al...

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High Theta and Low Alpha Powers May Be Indicative of BCI-Illiteracy in Motor Imagery

Cited by 159 publications

References 44 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

Performance of Brain–Computer Interfacing Based on Tactile Selective Sensation and Motor Imagery

On robust spatial filtering of EEG in nonstationary environments

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