Ricerc@Sapienza

Interpretability, accuracy and a solid neurophysiological basis can be considered as the main requirements for the classification model to monitor motor imagery tasks in post-stroke motor recovery paradigms supported by the brain-computer interface technology. This study aimed at comparing the accuracy performance of different classification approaches applied on a dataset of 15 stroke patients. We also explored how the variation in the dimensionality of the feature domain would influence the different classifier performance.

In an electroencephalographic (EEG)-based BCI-assisted Motor Imagery (MI) training the reinforcement of a specific EEG pattern elicited by correct MI requires that expert neurophysiologists with knowledge of BCI technology identify the optimal control features for each single patient. This procedure is highly dependent on the operator and is currently restricted to

Our objective was to investigate the capacity to control a P3-based brain-computer interface (BCI) device for communication and its related (temporal) attention processing in a sample of amyotrophic lateral sclerosis (ALS) patients with respect to healthy subjects. The ultimate goal was to corroborate the role of cognitive mechanisms in event-related potential (ERP)-based BCI control in ALS patients. Furthermore, the possible differences in such attentional mechanisms between the two groups were investigated in order to unveil possible alterations associated with the ALS condition.

The development of usable and accurate brain-computer interface (BCI) systems enables the transfer of this technology to clinical routine. When working with electroencephalographic signals (EEG), an important factor to optimize the signal to noise ratio of the signal is to choose the appropriate spatial filters.

Conditions of extreme neurological disability prevent any form of communication, even to show the emotional state. Brain Computer Interfaces (BCI) often use Electro-encephalography (EEG) measurements of the voluntary brain activity for driving a communication system. A BCI usage requires the activation of mental tasks. In the last few years, a new paradigm of activation has been used consisting in the autonomous brain activation through self-induced emotions, remembered on autobiographical basis.

In brain-computer interfaces (BCI), the detection of different mental states is a key element. In Motor Imagery (MI)-based BCIs, the considered features typically rely on the power spectral density (PSD) of brain signals, but alternative features can be explored looking for better performance. One possibility is the integration of functional connectivity (FC). These features quantify the interactions between different brain areas and they could represent a valuable tool to detect differences between two mental conditions.