DIY hybrid SSVEP-P300 LED stimuli for BCI platform using EMOTIV EEG headset

A High-Resolution LED Stimulator for Steady-State Visual Stimulation: Customizable, Affordable, and Open Source

Visually evoked steady-state potentials (SSVEPs) are neural responses elicited by visual stimuli oscillating at specific frequencies. In this study, we introduce a novel LED stimulator system explicitly designed for steady-state visual stimulation, offering precise control over visual stimulus parameters, including frequency resolution, luminance, and the ability to control the phase at the end of the stimulation. The LED stimulator provides a personalized, modular, and affordable option for experimental setups. Based on the Teensy 3.2 board, the stimulator utilizes direct digital synthesis and pulse width modulation techniques to control the LEDs. We validated its performance through four experiments: the first two measured LED light intensities directly, while the last two assessed the stimulator's impact on EEG recordings. The results demonstrate that the stimulator can deliver a stimulus suitable for generating SSVEPs with the desired frequency and phase resolution. As an open source resource, we provide comprehensive documentation, including all necessary codes and electrical diagrams, which facilitates the system's replication and adaptation for specific experimental requirements, enhancing its potential for widespread use in the field of neuroscience setups.

VEPdgets: Towards Richer Interaction Elements Based on Visually Evoked Potentials

For brain-computer interfaces, a variety of technologies and applications already exist. However, current approaches use visual evoked potentials (VEP) only as action triggers or in combination with other input technologies. This paper shows that the losing visually evoked potentials after looking away from a stimulus is a reliable temporal parameter. The associated latency can be used to control time-varying variables using the VEP. In this context, we introduced VEP interaction elements (VEP widgets) for a value input of numbers, which can be applied in various ways and is purely based on VEP technology. We carried out a user study in a desktop as well as in a virtual reality setting. The results for both settings showed that the temporal control approach using latency correction could be applied to the input of values using the proposed VEP widgets. Even though value input is not very accurate under untrained conditions, users could input numerical values. Our concept of applying latency correction to VEP widgets is not limited to the input of numbers.

Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI

The Steady State Visual Evoked Potential (SSVEP) is a widely used component in BCIs due to its high noise resistance and low equipment requirements. Recently, a novel SSVEP-based paradigm has been introduced for direction detection, in which, unlike the common SSVEP paradigms that use several frequency stimuli, only one flickering stimulus is used and it makes direction detection very challenging. So far, only the CCA method has been used for direction detection using SSVEP component analysis. Since Canonical Correlation Analysis (CCA) has some limitations, a Task-Related Component Analysis (TRCA) based method has been introduced for feature extraction to improve the direction detection performance. Although these methods have been proven efficient, they do not utilize the latent frequency information in the EEG signal. Therefore, the performance of direction detection using SSVEP component analysis is still suboptimal. For further improvement, the TRCA-based algorithm is enhanced by incorporating frequency information and introducing Spectrum-Enhanced TRCA (SE-TRCA). SE-TRCA method can utilize frequency information in conjunction with spatial information by concatenating the EEG signal and its shifted version. Accordingly, the obtained spatio-spectral filters perform as a Finite Impulse Response (FIR) filter. To evaluate the proposed SE-TRCA method, two different sorts of datasets (1) a hybrid BCI dataset (including SSVEP component for direction detection) and (2) a pure benchmark SSVEP dataset (including SSVEP component for frequency detection) have been used. Our experiments showed that the accuracy of direction detection using the proposed SE-TRCA and TRCA approaches compared to CCA-based approach have been increased by 23.35% and 28.24%, respectively. Furthermore, the accuracy of character recognition obtained from integrating P300 and SSVEP components in CCA, TRCA, and SETRCA approaches are 54.01%, 56.02%, and 58.56%, on the hybrid dataset, respectively. The evaluation of the SE-TRCA method on the benchmark SSVEP dataset demonstrates that the SE-TRCA method outperforms both CCA and TRCA, particularly regarding frequency detection accuracy. In this specific dataset, the SE-TRCA method achieved an impressive frequency detection accuracy of 98.19% for a 3-s signal, surpassing the accuracies of TRCA and CCA, which were 97.91% and 90.47%, respectively. These results demonstrated that the TRCA-based approach is more efficient than the CCA approach to extracting spatial filters. Moreover, SE-TRCA, extracting both Spectral and spatial information from the EEG signal, can capture more discriminative features from the SSVEP component and increase the accuracy of classification. The results of this study emphasize the effectiveness of the proposed SE-TRCA approach across different SSVEP paradigms and tasks. These findings provide strong evidence for the method's ability to generalize well in SSVEP analysis.