Designing a brain computer interface for control of an assistive robotic manipulator using steady state visually evoked potentials.

An assistive robotic manipulator (ARM) can provide independence and improve the quality of life for patients suffering from tetraplegia. However, to properly control such device to a satisfactory level without any motor functions requires a very high performing brain-computer interface (BCI). Steady-state visual evoked potentials (SSVEP) based BCI are among the best performing. Thus, this study investigates the design of a system for a full workspace control of a 7 degrees of freedom ARM. A SSVEP signal is elicited by observing a visual stimulus flickering at a specific frequency and phase. This study investigates the best combination of unique frequencies and phases to provide a 16-target BCI by testing three different systems off line. Furthermore, a fourth system is developed to investigate the impact of the stimulating monitor refresh rate. Experiments conducted on two subjects suggest that a 16-target BCI created by four unique frequencies and 16-unique phases provide the best performance. Subject 1 reaches a maximum estimated ITR of 235 bits/min while subject 2 reaches 140 bits/min. The findings suggest that the optimal SSVEP stimuli to generate 16 targets are a low number of frequencies and a high number of unique phases. Moreover, the findings do not suggest any need for considering the monitor refresh rate if stimuli are modulated using a sinusoidal signal sampled at the refresh rate.

The effect of type of afferent feedback timed with motor imagery on the induction of cortical plasticity.

A peripherally generated afferent volley that arrives at the peak negative (PN) phase during the movement related cortical potential (MRCP) induces significant plasticity at the cortical level in healthy individuals and chronic stroke patients. Transferring this type of associative brain-computer interface (BCI) intervention into the clinical setting requires that the proprioceptive input is comparable to the techniques implemented during the rehabilitation process. These consist mainly of functional electrical stimulation (FES) and passive movement induced by an actuated orthosis. In this study, we compared these two interventions (BCIFES and BCIpassive) where the afferent input was timed to arrive at the motor cortex during the PN of the MRCP. Twelve healthy participants attended two experimental sessions. They were asked to perform 30 dorsiflexion movements timed to a cue while continuous electroencephalographic (EEG) data were collected from FP1, Fz, FC1, FC2, C3, Cz, C4, CP1, CP2, and Pz, according to the standard international 10-20 system. MRCPs were extracted and the PN time calculated. Next, participants were asked to imagine the same movement 30 times while either FES (frequency: 20Hz, intensity: 8-35mAmp) or a passive ankle movement (amplitude and velocity matched to a normal gait cycle) was applied such that the first afferent inflow would coincide with the PN of the MRCP. The change in the output of the primary motor cortex (M1) was quantified by applying single transcranial magnetic stimuli to the area of M1 controlling the tibialis anterior (TA) muscle and measuring the motor evoked potential (MEP). Spinal changes were assessed pre and post by eliciting the TA stretch reflex. Both BCIFES and BCIpassive led to significant increases in the excitability of the cortical projections to TA (F(2,22)=4.44, p=0.024) without any concomitant changes at the spinal level. These effects were still present 30min after the cessation of both interventions. There was no significant main effect of intervention, F(1,11)=0.38, p=0.550, indicating that the changes in MEP occurred independently of the type of afferent inflow. An afferent volley generated from a passive movement or an electrical stimulus arrives at the somatosensory cortex at similar times. It is thus likely that the similar effects observed here are strictly due to the tight coupling in time between the afferent inflow and the PN of the MRCP. This provides further support to the associative nature of the proposed BCI system.

Processing movement related cortical potentials in EEG signals for identification of slow and fast movements.

The extraction of intended kinetic information from an EEG signal can have several applications related to the rehabilitation for subjects with various neurological disorders. However, the task is mainly constrained by the low signal-to-noise ratio for the EEG signals. It is well known that the cortical activity takes place at a very low frequency since it is characterized by the dropping of movement related cortical potential (MRCP) across the sampled EEG signal. The strong variations in the MRCP is indicative of the noise due to various sources. The aim of this work is to remove this noise from the EEG signals using empirical mode decomposition, which decomposes a signal into harmonics (intrinsic mode functions--IMF) of various frequencies. The IMFs pertaining to small frequencies are later used for features extraction where we extract the spatial and spectral features from the selected IMFs. The features are later used for classification using support vector machines (SVM). Our experiments show superior results to the benchmark method for the underlying dataset that has been used in this research.