Demos

Problem solving and visual search in a VR Escape Room game: A multi-modal approach
The escape room utilizes multi-modal bio-sensing, Cognionics EEG headband, BioVotion Armband (with 23 physiological data streams), eye gaze tracking module, and real-time stream record. For more information, please visit Dr. Tzyy-Ping's website: https://sccn.ucsd.edu/~jung/.

Our group recently developed and tested new Matlab-based toolbox, namely Real-time EEG Source-mapping Toolbox (REST), for real-time multi-channel EEG data analysis and visualization (Pion-Tonachini, Hsu et al., EMBC 2015; Pion-Tonachini, Hsu et al., 2018). Below is a video demo of the REST toolbox and its application to truly automatic EEG artifact removal. For more information, please visit Dr. Tzyy-Ping's website: https://sccn.ucsd.edu/~jung.

Real-time Automatic Artifact Rejection using REST 2.0

High-speed spelling with a noninvasive brain-computer interface.
Nakanishi, M., Wang, Y., Chen, X., Wang, Y.-T., Gao, X., Jung, T.-P., "Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis," IEEE Trans. Biomed. Eng., 65 (1): 104-12, 2018.

This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller. 
Methods: Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies. This study conducted a comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects. An online BCI speller was further implemented using a cue-guided target selection task with 20 subjects and a free-spelling task with 10 of the subjects. Results: The offline comparison results indicate that the proposed TRCA-based approach can significantly improve the classification accuracy compared with the extended CCA-based method. Furthermore, the online BCI speller achieved averaged information transfer rates (ITRs) of 325.33 ± 38.17 bits/min with the cue-guided task and 198.67 ± 50.48 bits/min with the free-spelling task. 
Conclusion: This study validated the efficiency of the proposed TRCA-based method in implementing a high-speed SSVEP-based BCI. Significance: The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control. For more information, please visit Dr. Tzyy-Ping's website: https://sccn.ucsd.edu/~jung.

High-speed spelling with a noninvasive brain-computer interface.
Proceedings of the National Academy of Sciences, Nov. 8, 2015.

This study reports a noninvasive brain speller that achieved a multi-fold increase in information transfer rate compared to other existing systems. Based on extremely precise coding of frequency and phase in single-trial steady-state visual evoked potentials (SSVEPs), this study developed a new joint frequency-phase modulation method and a user-specific decoding algorithm to implement synchronous modulation and demodulation of electroencephalogram (EEG). The resulting speller obtained high spelling rates up to 60 characters (12 words) per minute. The proposed methodological framework of high-speed BCI can lead to numerous applications in both patients with motor disabilities and healthy people. For more information, please visit Dr. Tzyy-Ping's website: https://sccn.ucsd.edu/~jung.

EEG signals are acquired by either dry EEG or commercially available EEG electrodes, miniture electronic circuits, and transmitted through Bluetooth telemetry to a cell phone. The cell phone is programed to assess fluctuations in individuals' alertness and capacity for performing cognitive tasks based on the received EEG signals. For more information, please visit Dr. Tzyy-Ping's website: https://sccn.ucsd.edu/~jung.

In the video, Yu-Te Wang, a co-investigator of this study, chooses a phone number to dial by looking at flickering numbers on a computer screen. The cell phone receives brainwaves via Bluetooth, decodes the signals associated with the selected numbers, and then places the call. This movie was filmed by another co-investigator of this study, Dr. Yijun Wang. For more information, please visit Dr. Tzyy-Ping's website: https://sccn.ucsd.edu/~jung.