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Despite partial success, communication has remained impossible for persons suffering from complete motor paralysis but intact cognitive and emotional processing, a state called com- plete locked-in state (CLIS). Based on a motor learning theoretical context and on the failure of neuroelectric brain–computer interface (BCI) communication attempts in CLIS, we here report BCI communication using functional near-infrared spectroscopy (fNIRS) and an implicit attentional processing procedure. Four patients suffering from advanced amyotro- phic lateral sclerosis (ALS)—two of them in permanent CLIS and two entering the CLIS with- out reliable means of communication—learned to answer personal questions with known answers and open questions all requiring a “yes” or “no” thought using frontocentral oxygen- ation changes measured with fNIRS. Three patients completed more than 46 sessions spread over several weeks, and one patient (patient W) completed 20 sessions. Online fNIRS classification of personal questions with known answers and open questions using linear support vector machine (SVM) resulted in an above-chance-level correct response rate over 70%. Electroencephalographic oscillations and electrooculographic signals did not exceed the chance-level threshold for correct communication despite occasional differences between the physiological signals representing a “yes” or “no” response. However, electro- encephalogram (EEG) changes in the theta-frequency band correlated with inferior commu- nication performance, probably because of decreased vigilance and attention. If replicated with ALS patients in CLIS, these positive results could indicate the first step towards aboli- tion of complete locked-in states, at least for ALS.

In a proof-of-principle prototypical demonstration we describe a new type of brain-machine interface (BMI) paradigm for upper limb motor training. The proposed technique allows a fast contingent and proportionally modulated stimulation of afferent proprioceptive and motor output neural pathways using operant learning. Continuous and immediate assisted-feedback of force proportional to rolandic rhythm oscillations during actual movements was employed and illustrated with a single case experiment. One hemiplegic patient was trained for two weeks coupling somatosensory brain oscillations with force field control during a robot mediated centre-out motor task whose execution approaches movements of everyday life. The robot facilitated actual movements adding a modulated force directed to the target, thus providing a non-delayed proprioceptive feedback. Neuro-electric, kinematic and motor-behavioural measures were recorded in pre- and post-assessments without force assistance. Patient’s healthy arm was used as control since neither a placebo control was possible nor other control conditions. We observed a generalized and significant kinematic improvement in the affected arm and a spatial accuracy improvement in both arms, together with an increase and focalization of the somatosensory rhythm changes used to provide assisted-force-feedback. The interpretation of the neurophysiological and kinematic evidences reported here is strictly related to the repetition of the motor-task and the presence of the assisted-force-feedback. Results are described as systematic observations only, without firm conclusions about the effectiveness of the methodology. In this prototypical view, the design of appropriate control conditions is discussed. This study presents a novel operant-learning-based BMI-application for motor training coupling brain oscillations and force feedback during an actual movement.

The paper deals with a specific kind of BCI application implemented with the aim of recovering the reaching ability of mild impaired stroke survivors. The overall idea is to take advantage of the plasticity of the brain to make the subject artificially learn alternative neural paths to control the arm movement again, by-passing the injured area thanks to a BCI system with an EEG-related force provided as a real-time feedback during the training period. Preliminary results have shown improvements in the kinematics of the upper limb motion of a first patient that performed this experimental rehabilitative program. Then, this BCI application is expected to enter soon the daily clinical practise as a useful tool besides the standard rehabilitation therapy.

Brain-computer interface (BCI) technology has been used for rehabilitation after stroke and there are a number of reports involving stroke patients in BCI-feedback training. Most publications have demonstrated the efficacy of BCI technology in post-stroke rehabilitation using output devices such as Functional Electrical Stimulation, robot, and orthosis. The aim of this review is to focus on the progress of BCI-based rehabilitation strategies and to underline future challenges. A brief history of clinical BCI-approaches is presented focusing on stroke motor rehabilitation. A context for three approaches of a BCI-based motor rehabilitation program is outlined: the substitutive strategy, classical conditioning and operant conditioning. Furthermore, we include an overview of a pilot study concerning a new neuro-forcefeedback strategy. This pilot study involved healthy participants. Finally we address some challenges for future BCI-based rehabilitation.