The Berlin Brain-Computer Interface: Non-Medical Uses of BCI Technology

Abstract

Brain-computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.

Keywords: BCI deficiency; brain–computer interface; decoding of mental states; event-related desynchronization; mental state monitoring; sensory motor rhythms.

Figure 1

Left: Feedback accuracy of all runs (gray dots) and intra-subject averages (black crosses). Right: Histogram of accuracies obtained in BBCI-controlled cursor movement task in all feedback runs of the study.

Figure 2

The graph shows the feedback performance of one BCI-naive subject from the very first trial on. Results are from one single session in which 8 runs of 100 trials (about 15 min) each have been recorded. There was no calibration period before. Feedback started with a general, subject-independent classifier which was adapted trial-by-trial. Dots indicate the average feedback performance (1D cursor control) of 20 trials. The mean performances of each run of 100 trials is shown as bars. The three colors relate to three different processing methods, which are explained in Section 2.4.

Figure 3

Left: Grand average of feedback performance within each run (horizontal bars and dots for each group of 20 trials) for subjects of Cat. I (N = 6), Cat. II (N = 2) and Cat. III (N = 3). An accuracy of 70% is assumed to be a threshold required for BCI applications. Note that all runs of one subject have been recorded within one session. Right: For one subject of Cat. III, spectra in channel CP3 and scalp topographies of band-power differences (signed r²-values) between the motor imagery conditions are compared between the beginning (runs 1 + 2) and the end (runs 7 + 8) of the experiment.

Figure 4

(A) Brain–computer interfacing reaction times are shown for all participants (asterisk) and grand average (solid line) separately for all three runs which had increasing time pressure to enforce faster BCI decision. As “BCI reaction time” we denote the latency from cue presentation until the decision of the user as conveyed by the BCI system. (B) The time-frequency plot displays the contrast (r² difference) between run 3 and run 1.

Figure 5

Left: Classification error for two groups of feedback trials with high and low prestimulus SMR amplitude. Classification was performed on a 1000 ms sliding window with 50 ms overlap and significant differences are denoted by “*” (black: p < 0. 01, gray p < 0.05). Right: ERD for the different groups of the two classes (high and low for left and right).

Figure 6

Left: Scalp distribution of the ERD in the prestimulus interval (−1000 to 0 ms) and the post-stimulus interval where the significant change in performance was observed (550–2650 ms).

Figure 7

Stimuli used for the suitcase inspection study. The upper row shows three examples of (simulated) X-rays of suitcases that do not contain a weapon. They had to be discriminated from suitcases in which there is a weapon hidden, like the three in the lower row (machine pistol, knife, and axe).

Figure 8

Left: Comparison of the concentration insufficiency index (CII, dotted curve) and the error index for the subject. The error index (the true performed errors smoothed over time) reflects the inverse of the arousal of the subject. Right: Correlation coefficient between the CII (returned by the classifier) and the true performance for different time shifts. Highest correlation is around a zero time shift, as expected. Note that the CII has an increased correlation with the error even before the error appears.

Figure 9

Experimental paradigm. The tertiary task was used to induce two different types of cognitive workload. An auditory task (AT) or mental calculation (MC) had to be performed in blocks of 2 min (high workload condition) interleaved with blocks of two duration without tertiary task (low workload condition). One run consisted of three pairs of blocks of high and low workload condition.

Figure 10

The exact time course of the classifier output for the best performing subject (lowerpanel), and the corresponding binary high/low workload indication used to control the mitigation (middle panel), in comparison with the true high and low workload conditions (upper panel) for auditory workload (95.6% correct).

Figure 11

Left: Chord distances according to Lerdahl's theory of tonal pitch space (Lerdahl, 2001) Right: The blue colored curve shows the subjective rating of participant VPcab which reflects only the distance of the fundamental tones, but does respect harmonical aspects. The orange colored line shows the output of the classifier. If reflects much better the musical structure of the stimuli than the behavioral data. (In the labels on the x-axis small and large font size corresponds to minor and major intervals).

Figure 12

Overview of a BCI system for the control of a pinball machine by motor imagery of, e.g., left and right hand imagined movements.

Figure 13

Brain–computer interfacing controlled tetris game. Left: A volunteer is playing a BCI-controlled version of the Tetris computer game. He uses left and right hand motor imagery to move the falling pieces horizontally, mental rotation to rotate it clockwise and foot motor imagery to let it drop. Right: The map shows the activation pattern during mental rotation in the tetris game (band-power in the beta-band 18–24 Hz with red color indicating event-related desynchronization, i.e., activation of the corresponding cortical area). The right parietal focus is in line with the literature.