Motor Imagery BCI with Auditory Feedback as a Mechanism for Assessment and Communication in Disorders of Consciousness

Coyle, Damien; Stow, Jacqueline; McCreadie, Karl; Sciacca, Nadia; McElligott, Jacinta; Carroll, Áine

doi:10.1007/978-3-319-57132-4_5

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…Currently, the clinical diagnosis of DOC patients is generally based on behavioral scales, such as the Coma Recovery Scale-Revised (CRS-R), which rely on overt motor responses to external stimuli at the time of observation (Seel et al, 2010 ). However, motor responses may be difficult to discern or inconsistent in this patient group, and it is becoming increasingly clear that relying on an overt behavioral response may result in misdiagnosis of a patients level of consciousness (Cruse and Owen, 2010 ; Coyle et al, 2017 ). In recent years, researchers have employed electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) techniques (Owen et al, 2002 ; Laureys et al, 2004 ; Di et al, 2007 ; Monti et al, 2010 ; Cruse et al, 2012a ; Li et al, 2015b ; Wang et al, 2017 ) to detect residual brain functions and provide motor-independent evidence of consciousness in certain patients with DOC (see Noirhomme et al, 2013 ; Kotchoubey, 2017 ; Lancioni et al, 2017 for reviews).…”

Section: Introductionmentioning

confidence: 99%

Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System

Pan

Xie

Huang

et al. 2018

Front. Hum. Neurosci.

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For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), detecting and assessing the residual cognitive functions of the brain remain challenging. Emotion-related cognitive functions are difficult to detect in patients with DOC using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised (CRS-R) because DOC patients have motor impairments and are unable to provide sufficient motor responses for emotion-related communication. In this study, we proposed an EEG-based brain-computer interface (BCI) system for emotion recognition in patients with DOC. Eight patients with DOC (5 VS and 3 MCS) and eight healthy controls participated in the BCI-based experiment. During the experiment, two movie clips flashed (appearing and disappearing) eight times with a random interstimulus interval between flashes to evoke P300 potentials. The subjects were instructed to focus on the crying or laughing movie clip and to count the flashes of the corresponding movie clip cued by instruction. The BCI system performed online P300 detection to determine which movie clip the patients responsed to and presented the result as feedback. Three of the eight patients and all eight healthy controls achieved online accuracies based on P300 detection that were significantly greater than chance level. P300 potentials were observed in the EEG signals from the three patients. These results indicated the three patients had abilities of emotion recognition and command following. Through spectral analysis, common spatial pattern (CSP) and differential entropy (DE) features in the delta, theta, alpha, beta, and gamma frequency bands were employed to classify the EEG signals during the crying and laughing movie clips. Two patients and all eight healthy controls achieved offline accuracies significantly greater than chance levels in the spectral analysis. Furthermore, stable topographic distribution patterns of CSP and DE features were observed in both the healthy subjects and these two patients. Our results suggest that cognitive experiments may be conducted using BCI systems in patients with DOC despite the inability of such patients to provide sufficient behavioral responses.

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Section: Introductionmentioning

confidence: 99%

Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System

Pan

Xie

Huang

et al. 2018

Front. Hum. Neurosci.

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“…In 2017 alone, this topic has been or will be presented in at least a dozen major conferences to our knowledge, including the Seventh International BCI Conference, Society for Neuroscience annual conference, and Human-Computer Interaction International (HCII) annual conference. This research direction was also recognized in our most recent book in this series [3].…”

Section: Introductionmentioning

confidence: 59%

“…In addition to communication and control applications, BCI technology can also be used for the assessment of cognitive functions of patients with disorders of consciousness (DOC) or locked-in syndrome (LIS) [1,2,3]; (Ortner et al, in press). The top-right corner of Fig.…”

Section: Introductionmentioning

confidence: 99%

Trends in BCI Research I: Brain-Computer Interfaces for Assessment of Patients with Locked-in Syndrome or Disorders of Consciousness

Guger

Coyle

Mattia

et al. 2017

SpringerBriefs in Electrical and Computer Engineering

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“…Notably, to unify the definition of emotion, researchers have focused on two main methods: one is to classify emotions into discrete categories (i.e., anger, fear, disgust, happiness, sadness, or surprise) [36] and more complex emotions based on combinations of these basic categories; the other method is to define emotion in several continuous dimensions (i.e., valence, arousal, and dominance) [37]. In the present study, we chose two typical emotional states and used a uniform label for each emotional state for the following reasons: 1) the ratings of valence and arousal using Self-Assessment Manikin (SAM) have large variations between subjects, and the meaning of the scales is subjective [38]; 2) the process of scoring the emotions of patients with DOC, who lack the ability to perform normal physical movements and have limited consciousness in the experiment, is difficult [39], [40]; and 3) considering too many emotional states may increase the burden on the patients.…”

Section: Stimulus Materialsmentioning

confidence: 99%

“…First, we compute the spectral power changes of each channel using a 512-point Short Time Fourier Transform (STFT) with a non-overlapped Hanning window of 1 second. Next, the band-power values are calculated by averaging the power values in each of the following frequency bands: delta (1-3 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (14-30 Hz), and gamma (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). Previous studies have shown that the EEG spectral amplitude is more linearly in the logarithmic scale than in the linear scale [41], [42].…”

Section: Online Signal Processingmentioning

confidence: 99%

An EEG-Based Brain Computer Interface for Emotion Recognition and Its Application in Patients with Disorder of Consciousness

Huang

Xie

Pan

et al. 2021

IEEE Trans. Affective Comput.

108

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Recognizing human emotions based on electroencephalogram (EEG) signals has received a great deal of attentions. Most of the existing studies focused on offline analysis, and real-time emotion recognition using a brain computer interface (BCI) approach remains to be further investigated. In this paper, we proposed an EEG-based BCI system for emotion recognition. Specifically, two classes of video clips that represented positive and negative emotions were presented to the subjects one by one, while the EEG data were collected and processed simultaneously, and instant feedback was provided after each clip. Ten healthy subjects participated in the experiment and achieved a high average online accuracy of 91.5% ± 6.34%. The experimental results demonstrated that the subjects emotions had been sufficiently evoked and efficiently recognized by our system. Clinically, patients with disorder of consciousness (DOC), such as coma, vegetative state, minimally conscious state and emergence minimally conscious state, suffer from motor impairment and generally cannot provide adequate emotion expressions. Consequently, doctors have difficulty in detecting the emotional states of these patients. Therefore, we applied our emotion recognition BCI system to patients with DOC. Eight DOC patients participated in our experiment, and three of them achieved significant online accuracy. The experimental results show that the proposed BCI system could be a promising tool to detect the emotional states of patients with DOC.

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Motor Imagery BCI with Auditory Feedback as a Mechanism for Assessment and Communication in Disorders of Consciousness

Cited by 6 publications

References 21 publications

Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System

Emotion-Related Consciousness Detection in Patients With Disorders of Consciousness Through an EEG-Based BCI System

Trends in BCI Research I: Brain-Computer Interfaces for Assessment of Patients with Locked-in Syndrome or Disorders of Consciousness

An EEG-Based Brain Computer Interface for Emotion Recognition and Its Application in Patients with Disorder of Consciousness

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