Interfaces cerveau-ordinateur,<i>locked-in syndrome</i>et troubles de la conscience

Lesenfants, Damien; Chatelle, Camille; Laureys, Steven; Noirhomme, Quentin

doi:10.1051/medsci/20153110017

Cited by 3 publications

(3 citation statements)

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“…Patients can be in coma for 2 to 4 weeks during which they are “unarousable”. This state is characterised by an absence of spontaneous eyes opening and muscle movements [ 2 , 3 ]. If and when patients emerge from this state, they can enter either a locked-in or a vegetative state, which in turn can transition to an MCS, or in the worst-case scenario, into permanent VS and/or death.…”

Section: Introductionmentioning

confidence: 99%

“…To assess DoC patients’ consciousness, most researches rely on their active participation using event-related potentials in particular, since it proves the patients’ ability to follow commands, which in turn is seen as proof of consciousness [ 2 ]. The stimuli used in this case can be auditory, tactile, visual or even olfactory [ 4 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Assessing consciousness in patients with disorders of consciousness using soft-clustering

Adama

Bogdan

2023

Brain Inf.

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Consciousness is something we experience in our everyday life, more especially between the time we wake up in the morning and go to sleep at night, but also during the rapid eye movement (REM) sleep stage. Disorders of consciousness (DoC) are states in which a person’s consciousness is damaged, possibly after a traumatic brain injury. Completely locked-in syndrome (CLIS) patients, on the other hand, display covert states of consciousness. Although they appear unconscious, their cognitive functions are mostly intact. Only, they cannot externally display it due to their quadriplegia and inability to speak. Determining these patients’ states constitutes a challenging task. The ultimate goal of the approach presented in this paper is to assess these CLIS patients consciousness states. EEG data from DoC patients are used here first, under the assumption that if the proposed approach is able to accurately assess their consciousness states, it will assuredly do so on CLIS patients too. This method combines different sets of features consisting of spectral, complexity and connectivity measures in order to increase the probability of correctly estimating their consciousness levels. The obtained results showed that the proposed approach was able to correctly estimate several DoC patients’ consciousness levels. This estimation is intended as a step prior attempting to communicate with them, in order to maximise the efficiency of brain–computer interfaces (BCI)-based communication systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing consciousness in patients with disorders of consciousness using soft-clustering

Adama

Bogdan

2023

Brain Inf.

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“…Patients can be in coma for two to four weeks during which they are "unarousable". This state is characterised by an absence of spontaneous eyes opening and muscle movements [2,8]. If and when patients emerge from this state, they can enter either a locked-in or a vegetative state, which in turn can transition to a MCS, or in the worst case scenario, into permanent VS and/or death.…”

Section: Introductionmentioning

confidence: 99%

Application of Soft-Clustering to Assess Consciousness in a CLIS Patient

Adama

Bogdan

2022

Brain Sciences

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Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients’ quality of life and prognosis. On the other hand, brain-computer interfaces (BCIs) provide a means for them to communicate using their brain signals. However, one major problem for such patients is the difficulty to determine if they are conscious or not at a specific time. This work aims to combine different sets of features consisting of spectral, complexity and connectivity measures, to increase the probability of correctly estimating CLIS patients’ consciousness levels. The proposed approach was tested on data from one CLIS patient, which is particular in the sense that the experimenter was able to point out one time frame Δt during which he was undoubtedly conscious. Results showed that the method presented in this paper was able to detect increases and decreases of the patient’s consciousness levels. More specifically, increases were observed during this Δt, corroborating the assertion of the experimenter reporting that the patient was definitely conscious then. Assessing the patients’ consciousness is intended as a step prior attempting to communicate with them, in order to maximize the efficiency of BCI-based communication systems.

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