EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review

Galiotta, Valentina; Quattrociocchi, Ilaria; D’Ippolito, Mariagrazia; Schettini, Francesca; Aricò, Pietro; Sdoia, Stefano; Formisano, Rita; Cincotti, Febo; Mattia, Donatella; Riccio, Angela

doi:10.3389/fnhum.2022.1040816

Cited by 10 publications

(7 citation statements)

References 129 publications

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“…In parallel, the EU guidelines aimed to offer recommendations for improving the diagnosis of pDoCs, with specific reference to data from a multimodal and multidisciplinary assessment by combining bedside clinical examination, neuroimaging, and neurophysiological evaluations (i.e., EEG) [ 4 ]. Additionally, the EU guidelines referred to advanced quantitative analysis of sleep, cortical responses to passive/resting-state paradigms measured by functional neuroimaging or neurophysiology, and complexity measures as the Perturbational Complexity Index (PCI) as promising strategies for detecting residual, not clinically evident, conscious activity in individuals with covert awareness [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

International survey on the implementation of the European and American guidelines on disorders of consciousness

Farisco,

Formisano,

Gosseries

et al. 2023

J Neurol

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Diagnostic, prognostic, and therapeutic procedures for patients with prolonged disorders of consciousness (pDoCs) vary significantly across countries and clinical settings, likely due to organizational factors (e.g., research vs. non-academic hospitals), expertise and availability of resources (e.g., financial and human). Two international guidelines, one from the European Academy of Neurology (EAN) and one from the American Academy of Neurology (AAN) in collaboration with the American Congress of Rehabilitation Medicine (ACRM) and the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR), were developed to facilitate consistent practice among professionals working with this challenging patient population. While the recommendations of both guidelines agree in principle, it remains an open issue how to implement them into clinical practice in the care pathway for patients with pDoCs. We conducted an online survey to explore health professional clinical practices related to the management of patients with pDoCs, and compare said practices with selected recommendations from both the guidelines. The survey revealed that while some recommendations are being followed, others are not and/or may require more honing/specificity to enhance their clinical utility. Particular attention should be given to the implementation of a multimodal assessment of residual consciousness, to the detection and treatment of pain, and to the impact of restrictions imposed by COVID-19 pandemics on the involvement of patients’ families/representatives.

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Section: Introductionmentioning

confidence: 99%

International survey on the implementation of the European and American guidelines on disorders of consciousness

Farisco,

Formisano,

Gosseries

et al. 2023

J Neurol

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“…Recent research proposed a multiple-scale convolutional few-shot learning network that can extract more information from EEG and applied it to evaluate DOC patients with residual consciousness in a P300-based brain-computer interface (BCIs) ( Pan et al, 2023 ). Although there have been preliminary results in the assistance of diagnosing DOC patients using BCIs, BCI tasks often require active cooperation from patients, which remains a significant challenge for DOC patients ( Galiotta et al, 2022 ). Another research attempted to maximally utilize functional connectivity information in brain networks to evaluate DOC, using convolutional neural networks and network reconfiguration techniques to classify patients with different consciousness states, achieving a classification accuracy of 87.2% ( Cai et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness

Yu,

Gao,

et al. 2023

Front. Neurosci.

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IntroductionIschemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology.MethodsIn our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC.ResultsBoth groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates’ temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%).DiscussionOur results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

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“…In addition, a 50 Hz notch filter was applied to reduce the electrical grid interface. Before performing the feature extraction and the following steps, to have a balanced distribution among the classes and provide adjusted chance level (Galiotta et al, 2022 ), 100 samples of 1,000 Hz EEG signals for the 5F (five classes) and NoMT (one class) paradigms were studied for each class as the preprocessing stage. Hence, a total of 600 trials were performed for one subject.…”

Section: Methodsmentioning

confidence: 99%

EEG-based finger movement classification with intrinsic time-scale decomposition

Degirmenci,

Yuce,

Perc

et al. 2024

Front. Hum. Neurosci.

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IntroductionBrain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals.MethodsIn this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not.ResultsAs a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems.DiscussionWhen compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).

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EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review

Cited by 10 publications

References 129 publications

International survey on the implementation of the European and American guidelines on disorders of consciousness

International survey on the implementation of the European and American guidelines on disorders of consciousness

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness

EEG-based finger movement classification with intrinsic time-scale decomposition

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