The identification of the intentionality of movement is a key-aspect for the development of brain-computer interfaces (BCIs) applicable to daily life in neurological patients. We present a novel method of processing of electroencephalography (EEG) signals for the extraction of movement intention in neurological patients with upper limb tremor. This method is based on event-related EEG desynchronization, considering α (8-12 Hz), β (13-30 Hz), and γ (30-40 Hz) bands. We have analyzed the EEG signals from the sensorimotor areas of 4 neurological patients presenting an upper limb tremor (grade 1 to 3/4) and executing successive finger-to-nose movements. A Quality Parameter (QP) for the detection of intentionality of movement has been extracted, by considering: (a) the changes in the β²/α and β/α ratio (representing bursts of β-γ frequencies) during the pre-movement period; (b) an appropriate threshold predicting the movement; (c) the number of movements executed. This QP allows the prediction of the voluntary movement with a probability between 70% and 90%. This method could be implemented in a wearable BCI to detect the intentionality of movement and could be used, for instance, to trigger the electrical stimulation in selected muscles of upper limbs with the aim of blocking the emergence of tremor.

Tremor is the most common movement disorder encountered during daily neurological practice. Tremor in the upper limbs causes functional disability and social inconvenience, impairing daily life activities. The response of tremor to pharmacotherapy is variable. Therefore, a combination of drugs is often required. Surgery is considered when the response to medications is not sufficient. However, about one third of patients are refractory to current treatments. New bioengineering therapies are emerging as possible alternatives. Our study was carried out in the framework of the European project “Tremor” (ICT-2007-224051). The main purpose of this challenging project was to develop and validate a new treatment for upper limb tremor based on the combination of functional electrical stimulation (FES; which has been shown to reduce upper limb tremor) with a brain-computer interface (BCI). A BCI-driven detection of voluntary movement is used to trigger FES in a closed-loop approach. Neurological tremor is detected using a matrix of EMG electrodes and inertial sensors embedded in a wearable textile. The identification of the intentionality of movement is a critical aspect to optimize this complex system. We propose a multimodal detection of the intentionality of movement by fusing signals from EEG, EMG and kinematic sensors (gyroscopes and accelerometry). Parameters of prediction of movement are extracted in order to provide global prediction plots and trigger FES properly. In particular, quality parameters (QPs) for the EEG signals, corticomuscular coherence and event-related desynchronization/synchronization (ERD/ERS) parameters are combined in an original algorithm which takes into account the refractoriness/responsiveness of tremor. A simulation study of the relationship between the threshold of ERD/ERS of artificial EEG traces and the QPs is also provided. Very interestingly, values of QPs were much greater than those obtained for the corticomuscular module alone.

Tremor is the most common movement disorder encountered during daily neurological practice. Tremor in the upper limbs causes functional disability and social inconvenience, impairing daily life activities. The response of tremor to pharmacotherapy is variable. Therefore, a combination of drugs is often required. Surgery is considered when the response to medications is not sufficient. However, about one third of patients are refractory to current treatments. New bioengineering therapies are emerging as possible alternatives. Our study was carried out in the framework of the European project "Tremor" (ICT-2007-224051). The main purpose of this challenging project was to develop and validate a new treatment for upper limb tremor based on the combination of functional electrical stimulation (FES; which has been shown to reduce upper limb tremor) with a brain-computer interface (BCI). A BCI-driven detection of voluntary movement is used to trigger FES in a closed-loop approach. Neurological tremor is detected using a matrix of EMG electrodes and inertial sensors embedded in a wearable textile. The identification of the intentionality of movement is a critical aspect to optimize this complex system. We propose a multimodal detection of the intentionality of movement by fusing signals from EEG, EMG and kinematic sensors (gyroscopes and accelerometry). Parameters of prediction of movement are extracted in order to provide global prediction plots and trigger FES properly. In particular, quality parameters (QPs) for the EEG signals, corticomuscular coherence and event-related desynchronization/synchronization (ERD/ERS) parameters are combined in an original algorithm which takes into account the refractoriness/responsiveness of tremor. A simulation study of the relationship between the threshold of ERD/ERS of artificial EEG traces and the QPs is also provided. Very interestingly, values of QPs were much greater than those obtained for the corticomuscular module alone.

We report Blood Oxygen Level Dependent (BOLD) signal changes recorded in the brain of an elite breath-hold diver during voluntary dry long breath-hold by means of fMRI. An independent component analysis (ICA) method was applied to extract brain areas that are putatively involved in the apnea process network. We discuss the hypothesis that these BOLD signal variations express the functional adaptive diving response under long apnea at rest. This is a preliminary report, which results are promising for large series investigations.

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