P300 Brain-Computer Interface-Based Drone Control in Virtual and Augmented Reality

Signal alignment for cross-datasets in P300 brain-computer interfaces

Objective.Transfer learning has become an important issue in the brain-computer interface (BCI) field, and studies on subject-to-subject transfer within the same dataset have been performed. However, few studies have been performed on dataset-to-dataset transfer, including paradigm-to-paradigm transfer. In this study, we propose a signal alignment (SA) for P300 event-related potential (ERP) signals that is intuitive, simple, computationally less expensive, and can be used for cross-dataset transfer learning.Approach.We proposed a linear SA that uses the P300's latency, amplitude scale, and reverse factor to transform signals. For evaluation, four datasets were introduced (two from conventional P300 Speller BCIs, one from a P300 Speller with face stimuli, and the last from a standard auditory oddball paradigm).Results.Although the standard approach without SA had an average precision (AP) score of 25.5%, the approach demonstrated a 35.8% AP score, and we observed that the number of subjects showing improvement was 36.0% on average. Particularly, we confirmed that the Speller dataset with face stimuli was more comparable with other datasets.Significance.We proposed a simple and intuitive way to align ERP signals that uses the characteristics of ERP signals. The results demonstrated the feasibility of cross-dataset transfer learning even between datasets with different paradigms.

Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces

Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of "mind-controlled," "controlling brain," "mind reading," and the ability to "download" or "upload" information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI.

Effect of Stimulus Size in a Visual ERP-Based BCI under RSVP

Rapid serial visual presentation (RSVP) is currently one of the most suitable paradigms for use with a visual brain-computer interface based on event-related potentials (ERP-BCI) by patients with a lack of ocular motility. However, gaze-independent paradigms have not been studied as closely as gaze-dependent ones, and variables such as the sizes of the stimuli presented have not yet been explored under RSVP. Hence, the aim of the present work is to assess whether stimulus size has an impact on ERP-BCI performance under the RSVP paradigm. Twelve participants tested the ERP-BCI under RSVP using three different stimulus sizes: small (0.1 × 0.1 cm), medium (1.9 × 1.8 cm), and large (20.05 × 19.9 cm) at 60 cm. The results showed significant differences in accuracy between the conditions; the larger the stimulus, the better the accuracy obtained. It was also shown that these differences were not due to incorrect perception of the stimuli since there was no effect from the size in a perceptual discrimination task. The present work therefore shows that stimulus size has an impact on the performance of an ERP-BCI under RSVP. This finding should be considered by future ERP-BCI proposals aimed at users who need gaze-independent systems.

Predictions of task using neural modeling

Introduction

A well-designed brain-computer interface (BCI) can make accurate and reliable predictions of a user's state through the passive assessment of their brain activity; in turn, BCI can inform an adaptive system (such as artificial intelligence, or AI) to intelligently and optimally aid the user to maximize the human-machine team (HMT) performance. Various groupings of spectro-temporal neural features have shown to predict the same underlying cognitive state (e.g., workload) but vary in their accuracy to generalize across contexts, experimental manipulations, and beyond a single session. In our work we address an outstanding challenge in neuroergonomic research: we quantify if (how) identified neural features and a chosen modeling approach will generalize to various manipulations defined by the same underlying psychological construct, (multi)task cognitive workload.

Methods

To do this, we train and test 20 different support vector machine (SVM) models, each given a subset of neural features as recommended from previous research or matching the capabilities of commercial devices. We compute each model's accuracy to predict which (monitoring, communications, tracking) and how many (one, two, or three) task(s) were completed simultaneously. Additionally, we investigate machine learning model accuracy to predict task(s) within- vs. between-sessions, all at the individual-level.

Results

Our results indicate gamma activity across all recording locations consistently outperformed all other subsets from the full model. Our work demonstrates that modelers must consider multiple types of manipulations which may each influence a common underlying psychological construct.

Discussion

We offer a novel and practical modeling solution for system designers to predict task through brain activity and suggest next steps in expanding our framework to further contribute to research and development in the neuroergonomics community. Further, we quantified the cost in model accuracy should one choose to deploy our BCI approach using a mobile EEG-systems with fewer electrodes-a practical recommendation from our work.

A separable convolutional neural network-based fast recognition method for AR-P300

Augmented reality-based brain–computer interface (AR–BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR–SSVEP system. In this study, a fast recognition method based on a separable convolutional neural network (SepCNN) was developed for an AR-based P300 component (AR–P300). SepCNN achieved single extraction of AR–P300 features and improved the recognition speed. A nine-target AR–P300 single-stimulus paradigm was designed to be administered with AR holographic glasses to verify the effectiveness of SepCNN. Compared with four classical algorithms, SepCNN significantly improved the average target recognition accuracy (81.1%) and information transmission rate (57.90 bits/min) of AR–P300 single extraction. SepCNN with single extraction also attained better results than classical algorithms with multiple averaging.

Application of virtual simulation situational model in Russian spatial preposition teaching

The purpose is to improve the teaching quality of Russian spatial prepositions in colleges. This work takes teaching Russian spatial prepositions as an example to study the key technologies in 3D Virtual Simulation (VS) teaching. 3D VS situational teaching is a high-end visual teaching technology. VS situation construction focuses on Human-Computer Interaction (HCI) to explore and present a realistic language teaching scene. Here, the Steady State Visual Evoked Potential (SSVEP) is used to control Brain-Computer Interface (BCI). An SSVEP-BCI system is constructed through the Hybrid Frequency-Phase Modulation (HFPM). The acquisition system can obtain the current SSVEP from the user's brain to know which module the user is watching to complete instructions encoded by the module. Experiments show that the recognition accuracy of the proposed SSVEP-BCI system based on HFPM increases with data length. When the data length is 0.6-s, the Information Transfer Rate (ITR) reaches the highest: 242.21 ± 46.88 bits/min. Therefore, a high-speed BCI character input system based on SSVEP is designed using HFPM. The main contribution of this work is to build a SSVEP-BCI system based on joint frequency phase modulation. It is better than the currently-known brain computer interface character input system, and is of great value to optimize the performance of the virtual simulation situation system for Russian spatial preposition teaching.