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Beyond the state of the art, towards intuitive and reliable non-visual Brain-Computer-Interfacing
(2016)
For the present work three main goals were formulated:
goal 1 To design a tactile BCI used for mobility which is
intuitive (G1.1), reliable and fast while being usable
by participants aged 50 years and above.
goal 2 To design an auditory BCI used for communication
which is intuitive and reliable.
goal 3 To examine the effects of training on tactile and
auditory BCI performance.
Three studies were performed to achieve these goals.
In the first study nine participants aged above 50 years
performed a five-session training after which eight participants
were able to navigate a virtual wheelchair with
mean accuracy above 95% and an ITR above 20 bits / min.
In the second study 15 participants, four of them endusers
with motor-impairment, were able to communicate
meaningful with high accuracies using an auditory BCI.
In the third study nine healthy and nine visually impaired
participants (regarded as sensory experts for non-visual
perception) performed tactile, auditory and visual (for
healthy participants only) copy tasks. Participants with
trained perception significantly outperformed control
participants for tactile but not for auditory performance.
Tactile performance of sensory experts was on equal levels
as the visual performance of control participants.
We were able to demonstrate viability of intuitive gazeindependent
tactile and auditory BCI. Our tactile BCI performed
on levels similar to those of visual BCI, outperforming
current tactile BCI protocols. Furthermore, we were
able to demonstrate significant beneficial effect of training
on tactile BCI performance. Our results demonstrate previously
untapped potential for tactile BCI and avenues for
future research in the field of gaze-independent BCI.
Feedback efficiency and training effects during alpha band modulation over the sensorimotor cortex
(2015)
Neural oscillations can be measured by electroencephalography (EEG) and these oscillations can be characterized by their frequency, amplitude and phase. The mechanistic properties of neural oscillations and their synchronization are able to explain various aspects of many cognitive functions such as motor control, memory, attention, information transfer across brain regions, segmentation of the sensory input and perception (Arnal and Giraud, 2012). The alpha band frequency is the dominant oscillation in the human brain. This oscillatory activity is found in the scalp EEG at frequencies around 8-13 Hz in all healthy adults (Makeig et al., 2002) and considerable interest has been generated in exploring EEG alpha oscillations with regard to their role in cognitive (Klimesch et al., 1993; Hanselmayr et al., 2005), sensorimotor (Birbaumer, 2006; Sauseng et al., 2009) and physiological (Lehmann, 1971; Niedermeyer, 1997; Kiyatkin, 2010) aspects of human life. The ability to voluntarily regulate the alpha amplitude can be learned with neurofeedback training and offers the possibility to control a brain-computer interface (BCI), a muscle independent interaction channel. BCI research is predominantly focused on the signal processing, the classification and the algorithms necessary to translate brain signals into control commands than on the person interacting with the technical system. The end-user must be properly trained to be able to successfully use the BCI and factors such as task instructions, training, and especially feedback can therefore play an important role in learning to control a BCI (Neumann and Kübler, 2003; Pfurtscheller et al., 2006, 2007; Allison and Neuper, 2010; Friedrich et al., 2012; Kaufmann et al., 2013; Lotte et al., 2013).
The main purpose of this thesis was to investigate how end-users can efficiently be trained to perform alpha band modulation recorded over their sensorimotor cortex. The herein presented work comprises three studies with healthy participants and participants with schizophrenia focusing on the effects of feedback and training time on cortical activation patterns and performance. In the first study, the application of a realistic visual feedback to support end-users in developing a concrete feeling of kinesthetic motor imagery was tested in 2D and 3D visualization modality during a single training session. Participants were able to elicit the typical event-related desynchronisation responses over sensorimotor cortex in both conditions but the most significant decrease in the alpha band power was obtained following the three-dimensional realistic visualization. The second study strengthen the hypothesis that an enriched visual feedback with information about the quality of the input signal supports an easier approach for motor imagery based BCI control and can help to enhance performance. Significantly better performance levels were measurable during five online training sessions in the groups with enriched feedback as compared to a conventional simple visual feedback group, without significant differences in performance between the unimodal (visual) and multimodal (auditory–visual) feedback modality. Furthermore, the last study, in which people with schizophrenia participated in multiple sessions with simple feedback, demonstrated that these patients can learn to voluntarily regulate their alpha band. Compared to the healthy group they required longer training times and could not achieve performance levels as high as the control group. Nonetheless, alpha neurofeedback training lead to a constant increase of the alpha resting power across all 20 training session.
To date only little is known about the effects of feedback and training time on BCI performance and cortical activation patterns. The presented work contributes to the evidence that healthy individuals can benefit from enriched feedback: A realistic presentation can support participants in getting a concrete feeling of motor imagery and enriched feedback, which instructs participants about the quality of their input signal can give support while learning to control the BCI. This thesis demonstrates that people with schizophrenia can learn to gain control of their alpha oscillations recorded over the sensorimotor cortex when participating in sufficient training sessions. In conclusion, this thesis improved current motor imagery BCI feedback protocols and enhanced our understanding of the interplay between feedback and BCI performance.
Objective: Brain Computer Interfaces (BCI) provide a muscle independent interaction channel making them particularly valuable for individuals with severe motor impairment. Thus, different BCI systems and applications have been proposed as assistive technology (AT) solutions for such patients. The most prominent system for communication utilizes event-related potentials (ERP) obtained from the electroencephalogram (EEG) to allow for communication on a character-by-character basis. Yet in their current state of technology, daily life use cases of such systems are rare. In addition to the high EEG preparation effort, one of the main reasons is the low information throughput compared to other existing AT solutions. Furthermore, when testing BCI systems in patients, a performance drop is usually observed compared to healthy users. Patients often display a low signal-to-noise ratio of the recorded EEG and detection of brain responses may be aggravated due to internally (e.g. spasm) or externally induced artifacts (e.g. from ventilation devices). Consequently, practical BCI systems need to cope with mani-fold inter-individual differences. Whilst these high demands lead to increasing complexity of the technology, daily life use of BCI systems requires straightforward setup including an easy-to-use graphical user interface that nonprofessionals can handle without expert support. Research questions of this thesis: This dissertation project aimed at bringing forward BCI technology toward a possible integration into end-users' daily life. Four basic research questions were addressed: (1) Can we identify performance predictors so that we can provide users with individual BCI solutions without the need of multiple, demanding testing sessions? (2) Can we provide complex BCI technology in an automated, user-friendly and easy-to-use manner, so that BCIs can be used without expert support at end-users' homes? (3) How can we account for and improve the low information transfer rates as compared to other existing assistive technology solutions? (4) How can we prevent the performance drop often seen when bringing BCI technology that was tested in healthy users to those with severe motor impairment? Results and discussion: (1) Heart rate variability (HRV) as an index of inhibitory control (i.e. the ability to allocate attention resources and inhibit distracting stimuli) was significantly related to ERP-BCI performance and accounted for almost 26% of variance. HRV is easy to assess from short heartbeat recordings and may thus serve as a performance predictor for ERP-BCIs. Due to missing software solutions for appropriate processing of artifacts in heartbeat data (electrocardiogram and inter-beat interval data), our own tool was developed that is available free of charge. To date, more than 100 researchers worldwide have requested the tool. Recently, a new version was developed and released together with a website (www.artiifact.de). (2) Furthermore, a study of this thesis demonstrated that BCI technology can be incorporated into easy-to-use software, including auto-calibration and predictive text entry. Naïve, healthy nonprofessionals were able to control the software without expert support and successfully spelled words using the auto-calibrated BCI. They reported that software handling was straightforward and that they would be able to explain the system to others. However, future research is required to study transfer of the results to patient samples. (3) The commonly used ERP-BCI paradigm was significantly improved. Instead of simply highlighting visually displayed characters as is usually done, pictures of famous faces were used as stimulus material. As a result, specific brain potentials involved in face recognition and face processing were elicited. The event-related EEG thus displayed an increased signal-to-noise ratio, which facilitated the detection of ERPs extremely well. Consequently, BCI performance was significantly increased. (4) The good results of this new face-flashing paradigm achieved with healthy participants transferred well to users with neurodegenerative disease. Using a face paradigm boosted information throughput. Importantly, two users who were highly inefficient with the commonly used paradigm displayed high accuracy when exposed to the face paradigm. The increased signal-to-noise ratio of the recorded EEG thus helped them to overcome their BCI inefficiency. Significance: The presented work at hand (1) successfully identified a physiological predictor of ERP-BCI performance, (2) proved the technology ready to be operated by naïve nonprofessionals without expert support, (3) significantly improved the commonly used spelling paradigm and (4) thereby displayed a way to effectively prevent BCI inefficiency in patients with neurodegenerative disease. Additionally, missing software solutions for appropriate handling of artifacts in heartbeat data encouraged development of our own software tool that is available to the research community free of charge. In sum, this thesis significantly improved current BCI technology and enhanced our understanding of physiological correlates of BCI performance.