Brain–Computer Interfaces - Index of

Non Invasive BCIs for Neuroprostheses Control of the Paralysed Hand 179
An example of such a system is given in Fig. 4c. In this case, the TH was
computed over a time period of 240 s by performing two “idle” runs without any
imagination. The band power was then extracted (15–19 Hz band) and the mean (x)
and standard deviation (sd) calculated. Here, TH was set to TH = x +3· sd. To
give the user enough time to changing his mental state, a refractory phase of 5 s was
implemented. In this time period, the classification output could not trigger another
grasp phase. The first two pictures of Fig. 7 presents two shots during drinking.
2.3.2 BCI-Training of Patient HK Using an Implanted Neuroprosthesis
For practical reasons, training was performed over 3 days at the patient’s home.
At first, the patient was asked to imagine different feet and left hand movements
to determine which movements required the least concentration. After this prescreening,
a cue-guided screening session was performed, where he was asked
to imagine feet and left hand movements 160 times. Applying time-frequency
analyses, the ERD/S maps were calculated. From these results, the most reactive frequency
bands (14–16 and 18–22 Hz) were selected, and a classifier was set up. With
this classifier, online feedback training was performed, which consisted of totally
25 training runs using the Basket paradigm [11]. The task in this Basket experiment
was to move a ball, falling with constant speed (falling duration was 3 s) from the
top of the screen, towards the indicated target (basket) to the left or to the rigth at the
bottom of the screen. Four runs (consisting of 40 trials each) with the best accuracy
were then used to adapt the frequency bands (resulting in 12–14 Hz and 18–22 Hz)
and calculate a new classifier (offline accuracy was 71 %). This classifier was then
used in an asynchronous paradigm for unguided training. Because of a significant
ERD, left hand MI was used for switching. This decision was supported by the fact
that, during the non-control state, foot activity was detected, so the classifier had a
bias to the foot class. Therefore, the output of the classifier was compared with a
threshold implemented in the class representing left hand movement imaginations
(compare the brain switch scheme given in Fig. 2). Whenever the classifier output
exceeded this threshold for a dwell time of 1 s, a switching signal was generated.
Consecutively a refractory period of 3 s was implemented so that the movement
control was stable and the probability of false positives was reduced. A 180-s run
without any MI showed no switch-action. After the BCI-training paradigm, the classifier
output of the BCI was coupled with the Freehand(R) system. In Fig. 5a the
averaged LDA output during the evaluation experiment is presented. The predefined
threshold in this case 0.7 was exceeded 1 s prior the real switching action
(dwell time) at second 0. Fig. 5b shows the corresponding band power features [14].
2.4 Interferences of Electrical Stimulation with the BCI
A grasp neuroprosthesis produces short electrical impulses with up to 40 mA
(= 40 V assuming 1 k� electrode-skin resistance) in a frequency range from 16
to 35 Hz. These strong stimulation pulses lead to interference in the μV-amplitude