Brain communication in the locked-in state

Abstract

Patients in the completely locked-in state have no means of communication and they represent the target population for braincomputer interface research in the last 15 years. Although different paradigms have been tested and different physiological signals used, to date no sufficiently documented completely locked-in state patient was able to control a brain-computer interface over an extended time period. We introduce Pavlovian semantic conditioning to enable basic communication in completely locked-in state. This novel paradigm is based on semantic conditioning for online classification of neuroelectric or any other physiological signals to discriminate between covert (cognitive) 'yes' and 'no' responses. The paradigm comprised the presentation of affirmative and negative statements used as conditioned stimuli, while the unconditioned stimulus consisted of electrical stimulation of the skin paired with affirmative statements. Three patients with advanced amyotrophic lateral sclerosis participated over an extended time period, one of which was in a completely locked-in state, the other two in the locked-in state. The patients' level of vigilance was assessed through auditory oddball procedures to study the correlation between vigilance level and the classifier's performance. The average online classification accuracies of slow cortical components of electroencephalographic signals were around chance level for all the patients. The use of a non-linear classifier in the offline classification procedure resulted in a substantial improvement of the accuracy in one locked-in state patient achieving 70% correct classification. A reliable level of performance in the completely locked-in state patient was not achieved uniformly throughout the 37 sessions despite intact cognitive processing capacity, but in some sessions communication accuracies up to 70% were achieved. Paradigm modifications are proposed. Rapid drop of vigilance was detected suggesting attentional variations or variations of circadian period as important factors in brain-computer interface communication with locked-in state and completely locked-in state.

The self-regulating brain and neurofeedback: Experimental science and clinical promise

“…Moreover, with one exception (Gallegos-Ayala et al, 2014), patients lacking control of all muscles (e.g., completely locked-in patients) have been entirely unsuccessful in maintaining control over neuroimaging signals (De Massari et al, 2013).…”

A Multi-Class Tactile Brain–Computer Interface Based on Stimulus-Induced Oscillatory Dynamics

“…Alternatively, visual P300 or steady-state visual evoked potential (SSVEP) from EEG can also be used for BCI systems [19], [20], but these fast visual BCI requires full engagement of the users' gaze control, which can be challenging and undesirable in real-life application settings. It is suggested that auditory and proprioceptive BCIs are the only remaining channels of communication for many potential BCI users, such as those in the late stage of amyotrophic lateral sclerosis (ALS) [21]- [23]. By requiring the active involvement of the somatosensory system, somatosensory stimulation might also provide a way to avoid the "extinction of thought" [24].…”

The effect of multimodal and enriched feedback on SMR-BCI performance

“…A limiting factor for the use of a traditional SMR based BCI is that vision must not be compromised in the end-user. For instance, several studies showed that in the last stage of ALS (i.e., completely locked-in) the visual sensory channel cannot be used as a reliable BCI input (De Massari et al, 2013;Murguialday et al, 2011). Sensorimotor rhythms (SMR) refer to localized sinusoidal frequencies in the upper alpha band (10-12 Hz) usually accompanied by changes in synchronization in the beta band (13-25 Hz) (Pfurtscheller and Neuper, 2001), which can be recorded over primary somatosensory and motor cortical areas.…”

“…Motor imagery is defined as the mental simulation of a kinesthetic movement (Decety and Inqvar, 1990;Neuper et al, 2005). Signal processing algorithms, individual users' characteristics, such as psychosocial and physiological parameters (e.g., fine motor skills) or brain structures, can predict performances for SMR-based BCIs Halder et al, 2011Halder et al, , 2013Hammer et al, 2011;Randolph, 2012). Besides these factors, feedback is a necessary feature for initial learning of the BCI skill (Brown, 1970;Kuhlman, 1978;McFarland et al, 1998;Wolpaw et al, 1991Wolpaw et al, , 2002.…”