Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses

Brain computer interface (BCI) technology has been proposed for motor neurorehabilitation, motor replacement and assistive technologies. It is an open question whether proprioceptive feedback affects the regulation of brain oscillations and therefore BCI control. We developed a BCI coupled on-line with a robotic hand exoskeleton for flexing and extending the fingers. 24 healthy participants performed five different tasks of closing and opening the hand: (1) motor imagery of the hand movement without any overt movement and without feedback, (2) motor imagery with movement as online feedback (participants see and feel their hand, with the exoskeleton moving according to their brain signals, (3) passive (the orthosis passively opens and closes the hand without imagery) and (4) active (overt) movement of the hand and rest. Performance was defined as the difference in power of the sensorimotor rhythm during motor task and rest and calculated offline for different tasks. Participants were divided in three groups depending on the feedback receiving during task 2 (the other tasks were the same for all participants). Group 1 (n = 9) received contingent positive feedback (participants' sensorimotor rhythm (SMR) desynchronization was directly linked to hand orthosis movements), group 2 (n = 8) contingent “negative” feedback (participants' sensorimotor rhythm synchronization was directly linked to hand orthosis movements) and group 3 (n = 7) sham feedback (no link between brain oscillations and orthosis movements). We observed that proprioceptive feedback (feeling and seeing hand movements) improved BCI performance significantly. Furthermore, in the contingent positive group only a significant motor learning effect was observed enhancing SMR desynchronization during motor imagery without feedback in time. Furthermore, we observed a significantly stronger SMR desynchronization in the contingent positive group compared to the other groups during active and passive movements. To summarize, we demonstrated that the use of contingent positive proprioceptive feedback BCI enhanced SMR desynchronization during motor tasks

Determination of Cycle to Cycle Battery Cell Degradation with High-Precision Measurements

Due to the long life of lithium ion cells, it is difficult to measure their low capacity degradation from cycle to cycle. In order to accelerate the measurements, cells are often exposed to extreme stress conditions, which usually means elevated temperatures and high charging currents. This raises doubts as to whether the results obtained in this way are representative for real world applications. This work shows that, with the help of very precise capacity measurements, it is possible to determine cell aging in a few days even under normal operating conditions from cycle to cycle. To verify this, a self-built measurement system is used. After demonstrating the capabilities of the system, two different cycling schemes are used simultaneously to determine the various causes of aging—namely cycle aging, calendrical aging and self-discharge due to leakage currents

Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS

A brain-computer interface (BCI) based on near-infrared spectroscopy (NIRS) could act as a tool for rehabilitation of stroke patients due to the neural activity induced by motor imagery aided by real-time feedback of hemodynamic activation. When combined with functional electrical stimulation (FES) of the affected limb, BCI is expected to have an even greater benefit due to the contingency established between motor imagery and afferent, haptic feedback from stimulation. Yet, few studies have explored such an approach, presumably due to the difficulty in dissociating and thus decoding the hemodynamic response (HDR) between motor imagery and peripheral stimulation. Here, for the first time, we demonstrate that NIRS signals elicited by motor imagery can be reliably discriminated from those due to FES, by first performing a univariate analysis of the NIRS signals, and subsequently by multivariate pattern classification. Our results showing that robust classification of motor imagery from the rest condition is possible support previous findings that imagery could be used to drive a BCI based on NIRS. More importantly, we demonstrate for the first time the successful classification of motor imagery and FES, indicating that it is technically feasible to implement a contingent NIRS-BCI with FES

Motor task power distributions.

<p>EEG frequency domain power topoplots for each motor task averaged over all participants of the contingent positive group (all 9 participants were right handed and performed the task with the right hand). The EEG power from 3 representative frequency bins (8–12; 12–18; 18–25 Hz) was averaged over the 5 seconds of each task and subtracted from the one obtained using the same process during rest. Red and blue color correspond to event related desynchronization (ERD) and to event related synchronization (ERS) with respect to rest in dB. The activity distribution is very similar for all motor tasks presenting a clear contralateral motor and parietal activation and an ipsilateral motor-pre-motor activation.</p

Experimental protocol.

<p>Where “CP” indicates contingent positive, “CN” contingent negative and “Sham” sham feedback group. In the second column the number of participants of each group, in the third the average age and in the forth column the handedness. The last column indicates the oscillation type (sensorimotor rhythm synchronization or desynchronization) used to compute the BCI performance.</p