Do the stimuli of an SSVEP-based BCI really have to be the same as the stimuli used for training it?

International audienceDoes the stimulation used during the training on an SSVEP-based BCI have to be similar to that of the end use? We recorded six-channel EEG data from 12 subjects in various conditions of distance between targets, and of difference in color between targets. Our analysis revealed that the stimulation configuration used for training which leads to the best classification accuracy is not always the one which is closest to the end use configuration. We found that the distance between targets during training is of little influence if the end use targets are close to each other, but that training at far distance can lead to a better accuracy for far distance end use (p < .01). Additionally, an interaction effect is observed between training and testing color (p < .001): while training with monochrome targets leads to good performance only when the test context involves monochrome targets as well, a classifier trained on colored targets can be efficient for both colored and monochrome targets. In a nutshell, in the context of SSVEP-based BCI, training using distant targets of different colors seems to lead to the best and more robust performance in all end use contexts

Exploiting code-modulating, Visually-Evoked Potentials for fast and flexible control via Brain-Computer Interfaces

Riechmann H. Exploiting code-modulating, Visually-Evoked Potentials for fast and flexible control via Brain-Computer Interfaces. Bielefeld: Universität Bielefeld; 2014

NeuroHub: Portable and Scalable Time Synchronization Instrument for Brain-Computer Interface and Functional Neuroimaging Research

The advent of new and improved brain imaging tools in recent decades has provided significant progress in understanding the physiological and neural bases of motor and cognitive processes and behavior. As neuroimaging and brain sensing technologies are further developed, they are miniaturized and become portable and wearable, allowing brain activity monitoring in ecologically-valid everyday environments. This introduces the possibility of using multiple systems concurrently on i) the same brain: multimodal/hybrid measurements for better identification of neurophysiological markers, and ii) multiple brains: hyperscanning for novel investigations of brain functions during social interactions. In all of these new directions, seamless integration of various neuroimaging systems is required. More specifically, precise time synchronization of acquired data streams is necessary for proper analysis and interpretation of results. However, there are currently no standards for interoperability and neuroimaging systems have many different designs and interfaces. Experiment setups using multiple systems may require extensive development for a customized solution that would need reconfiguration at the expense of additional time and effort, with the risk of possibly varying precision based on the custom solution. To address these issues, we have developed NeuroHub, a scalable device that can provide plug and play and reliable time synchronization by interfacing with common ports in neuroimaging systems. The device consists of a custom printed circuit board that fits atop an inexpensive and readily available development board for an Atmel ATmega2560 embedded microcontroller. It is housed in a 6 x 11 x 3.5 cm durable plastic casing, smaller than most smart phones, and includes BNC, serial, and parallel communication ports located around its perimeter. The device propagates any synchronization marker it receives from one of the ports and broadcasts it to all systems connected at other ports. The device can be extended as necessary by connecting multiple NeuroHub units. Verification and validation tests indicated reliable byte transmission with 100% accuracy of transmission and a consistent 1.020 millisecond latency in its standard configuration. A program was also developed for automated testing with Monte Carlo simulation, by sending and receiving event markers in various configurations. Through these tests, it became clear how unfit the use of multiple common computer ports is for sub-millisecond precise modality recording, due to their non-embedded nature. This problem is alleviated using NeuroHub as it allows synchronization of each computer through only one port. NeuroHub was implemented in two use cases to demonstrate its potential: i) Multimodal spatial navigation brain computer interface (BCI) that used simultaneous EEG and fNIR for enabling controlling actions within MazeSuite generated virtual environment. ii) Synthetic speech perception study which utilized two different fNIR systems simultaneously to record from a larger area. In the first use case, the naïve P300 response is used as a selection mechanism for a number of options for first-person navigation of a maze. fNIR measurements are used to assess if the person is attentive to the stimuli, which results in higher accuracy scores. For this setup to be successful, markers between the software for stimulation presentation, EEG recording, P300 analysis, maze presentation, and fNIR recording must be synchronized. In the second use case, subjects are presented with audio recordings of 5 sentences over 4 levels of quality of speech signal, ranging from natural speech to low quality synthesized speech, and asked to rate them for naturalness and intelligibility, while fNIR measurements are recorded to provide quantitative data about how cognitively taxing the synthesized speech is that has become common in everyday devices. In this experiment, information must be synchronized between the stimulus computer and the two fNIR recording devices. Both of these use cases demonstrate NeuroHub’s utility in next generation experiment setups with the goal of helping brain computer interface and functional neuroimaging research.M.S., Biomedical Engineering -- Drexel University, 201

Die Wirksamkeit von Feedback und Trainingseffekten während der Alphaband Modulation über dem menschlichen sensomotorischen Cortex

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.Die Wirksamkeit von Feedback und Trainingseffekten während der Alphaband Modulation über dem menschlichen sensomotorischen Corte