Tremor constitutes the most common movement disorder; in fact 14.5% of population between 50 to 89 years old suffers from it. Moreover, 65% of patients with upper limb tremor report disability when performing their activities of daily living (ADL). Unfortunately, 25% of patients do not respond to drugs or neurosurgery. In this regard, TREMOR project proposes functional compensation of upper limb tremors with a soft wearable robot that applies biomechanical loads through functional electrical stimulation (FES) of muscles. This wearable robot is driven by a Brain Neural Computer Interface (BNCI). This paper presents a multimodal BCI to assess generation, transmission and execution of both volitional and tremorous movements based on electroencephalography (EEG), electromyography (EMG) and inertial sensors (IMUs). These signals are combined to obtain: 1) the intention to perform a voluntary movement from cortical activity (EEG), 2) tremor onset, and an estimation of tremor frequency from muscle activation (EMG), and 3) instantaneous tremor amplitude and frequency from kinematic measurements (IMUs). Integration of this information will provide control signals to drive the FES-based wearable robot.
Rocon, E., Gallego, J.a., Barrios, L., Victoria, A.r., Ibanez, J., Farina, D., et al. (2010). Multimodal BCI-Mediated FES Suppression of Pathological Tremor. In IEEE Proceedings (pp.3337-3340) [10.1109/IEMBS.2010.5627914].
Multimodal BCI-Mediated FES Suppression of Pathological Tremor
CONFORTO, SILVIA;D'ALESSIO, Tommaso;SEVERINI, GIACOMO;
2010-01-01
Abstract
Tremor constitutes the most common movement disorder; in fact 14.5% of population between 50 to 89 years old suffers from it. Moreover, 65% of patients with upper limb tremor report disability when performing their activities of daily living (ADL). Unfortunately, 25% of patients do not respond to drugs or neurosurgery. In this regard, TREMOR project proposes functional compensation of upper limb tremors with a soft wearable robot that applies biomechanical loads through functional electrical stimulation (FES) of muscles. This wearable robot is driven by a Brain Neural Computer Interface (BNCI). This paper presents a multimodal BCI to assess generation, transmission and execution of both volitional and tremorous movements based on electroencephalography (EEG), electromyography (EMG) and inertial sensors (IMUs). These signals are combined to obtain: 1) the intention to perform a voluntary movement from cortical activity (EEG), 2) tremor onset, and an estimation of tremor frequency from muscle activation (EMG), and 3) instantaneous tremor amplitude and frequency from kinematic measurements (IMUs). Integration of this information will provide control signals to drive the FES-based wearable robot.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
