Severe motor disability affects functional cortical integration in the context of brain–computer interface (BCI) use

Nam, Chang S.; Woo, Jincheol; Bahn, Sangwoo

doi:10.1080/00140139.2011.647095

Cited by 26 publications

(22 citation statements)

References 38 publications

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“…However, the pattern of coherences may differ between healthy participants and patients with severe motor disabilities [33]. The reason for distinctive patterns between patients and healthy participants was interpreted by Nam et al [33] as reduced cortical differentiation and specialization in patients, reflected by a compensatory recruitment of more cortical regions. This difference may lead to lower performance of a BCI-system and thus, decrease the classification accuracy in patients.…”

Section: Discussionmentioning

confidence: 99%

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

et al. 2013

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Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53−.94) and power spectra (mean = .69; range = .40−.85). The coherence patterns in healthy participants did not match the expectation of central modulated -rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results.

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Section: Discussionmentioning

confidence: 99%

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

et al. 2013

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“…A lower BCI accuracy and information transfer rate was found for individuals in both the motor disabled groups (Nam et al, 2012). This was seen to occur alongside an increase in localized coherence during the task in healthy participants when compared to participants in the groups of motor impaired individuals.…”

Section: Discussionmentioning

confidence: 98%

“…In contrast, Nam et al (2012) compared functional integration, measured by coherence, during a P300 BCI control task performed by individuals with CP, ALS, and healthy controls. A lower BCI accuracy and information transfer rate was found for individuals in both the motor disabled groups (Nam et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Exploration of the neural correlates of cerebral palsy for sensorimotor BCI control

Daly¹,

Faller²,

Scherer³

et al. 2014

Front. Neuroeng.

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Cerebral palsy (CP) includes a broad range of disorders, which can result in impairment of posture and movement control. Brain-computer interfaces (BCIs) have been proposed as assistive devices for individuals with CP. Better understanding of the neural processing underlying motor control in affected individuals could lead to more targeted BCI rehabilitation and treatment options. We have explored well-known neural correlates of movement, including event-related desynchronization (ERD), phase synchrony, and a recently-introduced measure of phase dynamics, in participants with CP and healthy control participants. Although present, significantly less ERD and phase locking were found in the group with CP. Additionally, inter-group differences in phase dynamics were also significant. Taken together these findings suggest that users with CP exhibit lower levels of motor cortex activation during motor imagery, as reflected in lower levels of ongoing mu suppression and less functional connectivity. These differences indicate that development of BCIs for individuals with CP may pose additional challenges beyond those faced in providing BCIs to healthy individuals.

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“…Although the increased mobility of fNIRS has allowed for neuroimaging during real gait, research is still limited by poor temporal resolution. The use of electroencephalography (EEG) allows for higher temporal resolution and has potential for mobile applications (Jeon et al, 2011; Nam et al, 2011, 2012; Li and Nam, 2016). Advances in hardware systems have allowed for free movement in space, with wireless data transmission.…”

Section: Review Objectivesmentioning

confidence: 99%

Neuroimaging of Human Balance Control: A Systematic Review

Wittenberg

Thompson

Nam

et al. 2017

Front. Hum. Neurosci.

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128

106

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This review examined 83 articles using neuroimaging modalities to investigate the neural correlates underlying static and dynamic human balance control, with aims to support future mobile neuroimaging research in the balance control domain. Furthermore, this review analyzed the mobility of the neuroimaging hardware and research paradigms as well as the analytical methodology to identify and remove movement artifact in the acquired brain signal. We found that the majority of static balance control tasks utilized mechanical perturbations to invoke feet-in-place responses (27 out of 38 studies), while cognitive dual-task conditions were commonly used to challenge balance in dynamic balance control tasks (20 out of 32 studies). While frequency analysis and event related potential characteristics supported enhanced brain activation during static balance control, that in dynamic balance control studies was supported by spatial and frequency analysis. Twenty-three of the 50 studies utilizing EEG utilized independent component analysis to remove movement artifacts from the acquired brain signals. Lastly, only eight studies used truly mobile neuroimaging hardware systems. This review provides evidence to support an increase in brain activation in balance control tasks, regardless of mechanical, cognitive, or sensory challenges. Furthermore, the current body of literature demonstrates the use of advanced signal processing methodologies to analyze brain activity during movement. However, the static nature of neuroimaging hardware and conventional balance control paradigms prevent full mobility and limit our knowledge of neural mechanisms underlying balance control.

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Severe motor disability affects functional cortical integration in the context of brain–computer interface (BCI) use

Cited by 26 publications

References 38 publications

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

Exploration of the neural correlates of cerebral palsy for sensorimotor BCI control

Neuroimaging of Human Balance Control: A Systematic Review

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