Abstract
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.
Highlights
Patients with severe brain injury may suffer from disorders of consciousness (DOC), including coma, vegetative state (VS) and minimally conscious state (MCS)
Several studies have reported emotion recognition deficits in patients with schizophrenia (Kohler et al, 2000; Taylor and Iii, 2012; Kayser et al, 2014; Corcoran et al, 2015; Bilgi et al, 2017), and the results have suggested that impairments in auditory, olfactory, or visual function may lead to deficits in emotion recognition
Emotion recognition tasks have been included in the Functional Emotional Assessment Scale (FEAS), the Development Neuropsychological Assessment-II (NEPSYII) and the Montreal Cognitive Assessment (MoCA), which are commonly used to evaluate cognitive impairments in patients with schizophrenia, attention deficit hyperactivity disorder (ADHD), and Parkinsons disease (Solomon et al, 2007; Marneweck and Hammond, 2014; Pitzianti et al, 2017)
Summary
Patients with severe brain injury may suffer from disorders of consciousness (DOC), including coma, vegetative state (VS) and minimally conscious state (MCS). Keystones in diagnosing these disorders are the acquisition of voluntary responses, such as command following and functional communication, which indicate emergence from VS and MCS, respectively (Noirhomme et al, 2013). Emotion recognition tasks have been included in the Functional Emotional Assessment Scale (FEAS), the Development Neuropsychological Assessment-II (NEPSYII) and the Montreal Cognitive Assessment (MoCA), which are commonly used to evaluate cognitive impairments in patients with schizophrenia, attention deficit hyperactivity disorder (ADHD), and Parkinsons disease (Solomon et al, 2007; Marneweck and Hammond, 2014; Pitzianti et al, 2017). By exploring emotion recognition in patients with DOC, we may be able to more thoroughly evaluate residual cognitive functions and determine the extent to which the multiple brain functions associated with emotion recognition are impaired after severe brain injury
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