Assessment and Intervention with Patients with Severe Disorders of Consciousness

Lancioni, Giulio E.; Bosco, Andrea; O’Reilly, Mark F.; Sigafoos, Jeff; Belardinelli, Marta Olivetti

doi:10.1007/s41252-017-0025-5

Cited by 7 publications

(12 citation statements)

References 108 publications

(130 reference statements)

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“…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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“…The current literature indicates several common ways to assess consciousness in people suffering from acquired brain injuries ABI. Many of them involve some use of EEG signal measurement, however, techniques such as transcranial stimulation, deep brain stimulation are also possible, and they were described in the literature [2]. EEG-based techniques often apply measurement and analysis of event-related potentials (ERP) [2].…”

Section: Introductionmentioning

confidence: 99%

“…Many of them involve some use of EEG signal measurement, however, techniques such as transcranial stimulation, deep brain stimulation are also possible, and they were described in the literature [2]. EEG-based techniques often apply measurement and analysis of event-related potentials (ERP) [2]. They also may involve using external stimuli such as auditory signals [3].…”

Section: Introductionmentioning

confidence: 99%

“…Although positron emission tomography (PET) and functional magnetic resonance imaging fMRI are also employed for medical diagnostics, they are not standard medical procedures, because of their high cost, thus they are more often used for research purposes [9]. Some works also identify the need for cheap and wide-applicable solutions for the estimation of the state of patients suffering from ABI [2]. Researchers and physicians have proved that the use of PET and especially fMRI is eventually inevitable in the clinical examination of patients with disorders of consciousness [10][11][12][13] due to their accuracy and usability.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, an approach applying an eye-tracker device was employed to assess the emotional states of healthy participants [17]. There are also works that identify the need for cheap and widely applicable solutions for the estimation of the state of patients suffering from ABI [2]. We would like to propose a direction of diagnosis based upon the bimodal measurement of the activity of people taking part in therapeutic sessions.…”

Section: Introductionmentioning

confidence: 99%

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Multifactor consciousness level assessment of participants with acquired brain injuries employing human–computer interfaces

et al. 2020

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Background: A lack of communication with people suffering from acquired brain injuries may lead to drawing erroneous conclusions regarding the diagnosis or therapy of patients. Information technology and neuroscience make it possible to enhance the diagnostic and rehabilitation process of patients with traumatic brain injury or posthypoxia. In this paper, we present a new method for evaluation possibility of communication and the assessment of such patients' state employing future generation computers extended with advanced human-machine interfaces.Methods: First, the hearing abilities of 33 participants in the state of coma were evaluated using auditory brainstem response measurements (ABR). Next, a series of interactive computer-based exercise sessions were performed with the therapist's assistance. Participants' actions were monitored with an eye-gaze tracking (EGT) device and with an electroencephalogram EEG monitoring headset. The data gathered were processed with the use of data clustering techniques.Results: Analysis showed that the data gathered and the computer-based methods developed for their processing are suitable for evaluating the participants' responses to stimuli. Parameters obtained from EEG signals and eye-tracker data were correlated with Glasgow Coma Scale (GCS) scores and enabled separation between GCS-related classes. The results show that in the EEG and eye-tracker signals, there are specific consciousness-related states discoverable. We observe them as outliers in diagrams on the decision space generated by the autoencoder. For this reason, the numerical variable that separates particular groups of people with the same GCS is the variance of the distance of points from the cluster center that the autoencoder generates. The higher the GCS score, the greater the variance in most cases. The results proved to be statistically significant in this context. Conclusions:The results indicate that the method proposed may help to assess the consciousness state of participants in an objective manner.

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Brain-Computer Interfaces in Disorders of Consciousness

Yang

et al. 2022

Neurosci. Bull.

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Assessment and Intervention with Patients with Severe Disorders of Consciousness

Cited by 7 publications

References 108 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

Multifactor consciousness level assessment of participants with acquired brain injuries employing human–computer interfaces

Brain-Computer Interfaces in Disorders of Consciousness

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