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While decades of research have investigated and technically improved brain–computer interface (BCI)-controlled applications, relatively little is known about the psychological aspects of brain–computer interfacing. In 35 healthy students, we investigated whether extrinsic motivation manipulated via monetary reward and emotional state manipulated via video and music would influence behavioral and psychophysiological measures of performance with a sensorimotor rhythm (SMR)-based BCI. We found increased task-related brain activity in extrinsically motivated (rewarded) as compared with nonmotivated participants but no clear effect of emotional state manipulation. Our experiment investigated the short-term effect of motivation and emotion manipulation in a group of young healthy subjects, and thus, the significance for patients in the locked-in state, who may be in need of a BCI, remains to be investigated.

Brain computer interfaces (BCIs) allow for controlling devices through modulation of sensorimotor rhythms (SMR), yet a profound number of users is unable to achieve sufficient accuracy. Here, we investigated if visuo-motor coordination (VMC) training or Jacobsen's progressive muscle relaxation (PMR) prior to BCI use would increase later performance compared to a control group who performed a reading task (CG). Running the study in two different BCI-labs, we achieved a joint sample size of N = 154 naive participants. No significant effect of either intervention (VMC, PMR, control) was found on resulting BCI performance. Relaxation level and visuo-motor performance were associated with later BCI performance in one BCI-lab but not in the other. These mixed results do not indicate a strong potential of VMC or PMR for boosting performance. Yet further research with different training parameters or experimental designs is needed to complete the picture.

Brain-computer interfaces (BCIs) establish a link between brain and computer by creating a communication path independent from muscle control. BCIs based on visual event-related potentials (ERPs) depend on the perception of external stimulations and this makes them ill-designed for people with impaired gaze. To approach gaze-independent spelling, we have designed a visual ERP BCI using a P300 matrix speller based on mental imagery. The predefined intensification sequences were intuitively arranged and known by participants. By gradually reducing the intensification opacity from 100% to 0%, participants were guided on mentally imagining the intensifications in rhythm with auditory cues. This study investigated whether imagined visual stimulation can lead to ERP elicitation. The results of testing our paradigm with 15 healthy participants proved that ERPs were still significantly elicited during the mental imagery.

The so-called P300-BCI provided high information transfer rates if stimuli are presented in the visual modality in healthy participants and those with severe neurological disease alike. Visual presentation of stimuli constitutes a severe obstacle to those with no control of eye movement. The following study investigated a potential alternative to using any sensory modality for stimulation. Instead healthy participants were instructed to imagine the traditional flashing matrix. To facilitate such imagery, stimuli were first presented sequentially in the visual and auditory domain. In Experiment 1 (N = 10) we found a decline in performance when stimuli were presented sequentially as compared to randomly, but still a P300 was identifiable albeit lower in amplitude. In Experiment 2 (N = 23) the matrix was still presented as a support, but no visual stimulation occurred. Instead the stimulation frequency was indicated by auditory clicks in condition 1 and removed altogether in condition 2. The P300 amplitude was significantly higher in target than non-target stimulations in both conditions, and higher in the no stimulation condition than in the auditory condition. Selection accuracy was above chance level for 8 participants in the auditory and for 10 in the no stimulation condition. Taken together results indicate that a P300 can be generated by imagined stimulation, but the paradigm requires further investigation and improvement.

We describe how the classic P300 based BCI-controlled spelling paradigm was transformed into a painting tool. Initiated by an artist, we (“we” refers to all people who were involved in the making of Brain Painting at any one time. “I”, “me” or “AK” refers to the first author. Individual names/initials are used to identify a specific person being involved in a specific step of the history including end-users with disease) developed Brain Painting in close interaction with two end-users of the technology in the locked-in state due to amyotrophic lateral sclerosis. We enabled them to use Brain Painting at their home independent of the researchers being present. Throughout the use of Brain Painting we applied measures of effectiveness, efficiency, and end-user satisfaction leading to a unique long-term evaluation covering hundreds of sessions. We demonstrated a positive effect on quality of life, self-esteem, and other measures of well-being. The artists created a plethora of Brain Paintings, which have been shown at several exhibitions. In this chapter, we describe firstly, the history of Brain Painting. Secondly, we explain in detail how the P300-BCI controlled application works. Thirdly, we dwell on how we enabled use of Brain Painting in daily life at the end-users’ home. We, fourthly, dedicate a separate section to the impact of Brain Painting on quality of life, and show finally some of the Brain Paintings and summarize the artists’ Brain Painting exhibitions. We end with a summary, emphasising that if BCI developers follow the user-centred design, i.e., listen to and interact closely with the targeted end-users of technology, the transfer of BCI-controlled applications out of the laboratory to the end-users’ home becomes more likely, feasible and hopefully more real.

Brain–computer interfaces (BCI) translate brain activity into control signals or commands for a device. Motor imagery of the limbs allows for modulating the sensorimotor rhythms (SMR), but there are up to 30% of the participants for whom electroencephalography (EEG) based SMR-BCI cannot detect any imagery-related changes. Individual variables, such as ability to concentrate on a task and error duration in a two-hand visuomotor coordination (VMC) task have been previously found to predict accuracy in an SMR-BCI. A first study attempted to substantiate those predictors by introducing a 30 min relaxation or VMC training period prior to an SMR-BCI session, but performance did not increase when compared to a control group. As the predictor training may have been too short, we applied 4 such training sessions on consecutive days in the current study. In a pre–post design, SMR-BCI accuracy of n = 39 participants increased from session 1 before to session 2 after the predictor training. While the manipulation of the predictor variables was successful, there was no effect on SMR-BCI performance. BCI accuracy correlated positively with the neurophysiological SMR predictor identified by Blankertz et al. [3] , consolidating its predictive value, and with the state mindfulness scale. No other psychological predictor could be identified or replicated. Further studies should therefore focus more on delineating (partially) replicated or potential predictors such as VMC or mindfulness to help refining a sound model to predict SMR-BCI accuracy.

This study reports the use of Brain Painting, a BCI application for creative expression, which was implemented at the homes of two end-users in the locked-in state. Both are artists who had gradually lost the ability to paint after being diagnosed with amyotrophic lateral sclerosis. The usability of the BCI in expert-independent use was evaluated by the end-users. End-user HP painted in 152 sessions within 22 months (in total: 255 h), end-user JT in 158 sessions within 15 months (in total: 168 h). With an accuracy of 70–90%, BCI control was mostly moderate to high. Both end-users were highly satisfied with the BCI as an assistive device. Brain Painting re-enabled them to be creatively active in their home environment and thus improved their feelings of happiness, usefulness, self-esteem, well-being, and consequently quality of life, and supports social inclusion. This long-term evaluation study suggests that BCI is a valuable tool for people in the locked-in state.

Brain Painting is a brain-computer interface (BCI) application that allows for painting on a virtual canvas without requiring motor input. Following the user-centered design, we developed Brain Painting V2 (BP2) for two end-users with ALS (‘HP’ and ‘JT’) who use Brain Painting independently at home (BP1). After including line drawing and multiple shapes in BP2, we report the evaluation of 27 home use days by JT over 3.5 months. On scales from 0 to 10, the end-user indicated good satisfaction M = 7.1 (SD = 1.7), good enjoyment M = 6.9 (SD = 1.9) and low frustration M = 2.3 (SD = 2.5). Workload as measured once with the NASA Task Load Index was substantial (65/100). Satisfaction as assessed once with the QUEST2.0 was high (4.8/5) and maximum (5/5) for the BCI-specific aspects. Level of control predicted satisfaction, indicating the importance of maintaining high BCI accuracy. As results are based on one end-user only, they cannot be generalized to the population of potential BP2 end-users. However, the high...

Reliable predictors of BCI performance would be desirable for basic research and application of BCI in a clinical context alike. In basic research, predictors help to elucidate how the brain instantiates BCI control. With respect to BCI controlled applications to be used by patient end-users with disease, predictors could support the choice of the optimal brain signal. Training of the predicting variable may support later BCI control. Among others, physiologic and psychologic variables have been suggested as such predictors. For example, the resting state μ-rhythm peak, the activation of dorsolateral prefrontal cortex during motor imagery, and the ability to coordinate visual and motor information were related to performance in different motor imagery BCI paradigms. The predictive power was low to medium, few even high, where the physiologic predictor was most powerful. To identify predictors, those and the related criterion variable have to be unambiguously defined. Likewise, reliability and validity have to be specified in the realm of BCI.

As the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox). Source localizations were found to be more stable across reconstruction pipelines than subsequent estimations of functional connectivity, while effective connectivity estimates where the least consistent. All results were relatively unaffected by the choice of the electrical head model, while the choice of the inverse method and source imaging package induced a considerable variability. In particular, a relatively strong difference was found between LCMV beamformer solutions on one hand and eLORETA/WMNE distributed inverse solutions on the other hand. We also observed a gradual decrease of consistency when results are compared between studies, within individual participants, and between individual participants. In order to provide reliable findings in the face of the observed variability, additional simulations involving interacting brain sources are required. Meanwhile, we encourage verification of the obtained results using more than one source imaging procedure.

Brain painting (BP) is non-invasive electroencephalography (EEG) based Brain-Computer Interface (BCI) for creative expression based on a P300 matrix. The technology was transferred into a home setup for two patients with amyotrophic lateral sclerosis (ALS), who used the system for several years while being evaluated on performance and satisfaction. Holz and colleagues found that the use of BP increased quality of life. Additionally, they described that changes in the amplitude of the P300 ERPs could be observed between recalibrations of the BCI. In this paper, we quantified the evolution of the P300 peaks in the two BCI end-users (HP and JT). For HP, the P300 peak amplitude increased during 9 months, then progressively decreased for the following 51 months, but the BCI accuracy remained stable. JT’s P300 peak amplitude did not significantly decrease during 32 months that separated the calibrations. Yet, JT’s BCI accuracy declined which we may attribute to a decline in physical functioning due to ALS. Painters used online BCI for hundreds of hours (HP 755, JT 223) and both finished more than 50 named brain paintings. HP could use BP autonomously and regularly at home for 33 months without recalibration of the system, and JT for 10 months, suggesting the stability of P300 and SWLDA online classifiers in the long-term, and demonstrating the feasibility of having a P300 based system at home that requires few involvement of BCI experts.

Proceedings of the 6th International Brain-Computer Interface Conference 2014