An instrument for remote control of the robot by wearable brain–computer interface (BCI) is proposed for rehabilitating children with attention-deficit/hyperactivity disorder (ADHD). Augmented reality (AR) glasses generate flickering stimuli, and a single-channel electroencephalographic BCI detects the elicited steady-state visual evoked potentials (SSVEPs). This allows benefiting from the SSVEP robustness by leaving available the view of robot movements. Together with the lack of training, a single channel maximizes the device’s wearability, fundamental for the acceptance by ADHD children. Effectively controlling the movements of a robot through a new channel enhances rehabilitation engagement and effectiveness. A case study at an accredited rehabilitation center on ten healthy adult subjects highlighted an average accuracy higher than 83%, with information transfer rate (ITR) up to 39 b/min. Preliminary further tests on four ADHD patients between six- and eight-years old provided highly positive feedback on device acceptance and attentional performance.

Wearable Brain–Computer Interface Instrumentation for Robot-Based Rehabilitation by Augmented Reality

This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady- State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies providea significantenhancement of theSSVEPclassification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1- $\sigma$ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems.

Enhancement of SSVEPs Classification in BCI-Based Wearable Instrumentation Through Machine Learning Techniques

Performance enhancement of wearable instrumentation for AR-based SSVEP BCI

Steady-State-Visually-Evoked-Potential (SSVEP) Brain-Computer Interfaces (BCIs) make use of flickering stimuli to determine the target a user is looking at and select commands accordingly. Those types of BCI can be operated with little to no training, achieve high classification accuracies and are robust in application. A drawback of this approach is the reduced user comfort due to the constant flickering of the stimuli which can be annoying and tiring to look at. Existing studies addressing this issue try to make use of motion to disguise the oscillating patterns. However, this makes them look abstract and restricts the design of those applications as those patterns do not blend in to conventional user interfaces. In this work we introduce the concept of spinning icons to evoke SSVEPs. The icons are rotating in a certain frequency around their vertical axis and are supposed to appear more natural and be less stressing for the human eye. Furthermore this concept is not bound to any kind of abstract motion based pattern but rather supposed to work with any type of icon or image. The newly designed stimuli were evaluated in an application-oriented scenario and compared to standard and state-of-the-art movement-based SSVEP stimuli regarding the classification accuracy and experienced visual fatigue. The results show that the newly created stimuli performed equally well and partially even better in terms of classification accuracy and were rated throughout better concerning visual fatigue by the study participants. This work therefore lays the foundation for more comfortable SSVEP-BCIs which can be used with basically every icon or UI element spinning around their vertical axis.

Spinning Icons: Introducing a Novel SSVEP-BCI Paradigm Based on Rotation

A Brain-Computer Interface (BCI) speller system based on the Steady-State Visually Evoked Potentials (SSVEP) paradigm is presented. The potentials are elicited through the gaze fixation at one out of the four checkerboards shown on screen, which are flickering at 5, 12, 15 and 20 Hz. After the feature extraction, two dimensionality reduction algorithms, Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are implemented, in order to compare the accuracy obtained by the Support Vector Machines (SVM) and the Canonical Correlation Analysis (CCA) as classification algorithms. The BCI system is optimized by analyzing the duration of the records, the number of training data sets, and the electroencephalography (EEG) recording positions on the scalp, taking into account the Oz, O1, O2, Pz, P3, P4, T5 and T6 positions according to the 10–10 EEG reference system. Experiments are carried out with twenty test subjects, so as to validate statistically that the system achieves 88.90% accuracy on recognizing the stimulus stared by the user, where CCA achieves slightly better results than the LDA + SVM linear kernel classifier. However, the experimental results of the spelling task show that the personalized BCI system allows the user to reach an efficiency of 2.87 correctly selected characters per minute and an accuracy of 92.36% for the above the minimum accuracy threshold users.

Comparison between the Canonical Correlation Analysis and the Support Vector Machines as classification algorithms in an SSVEP-based Brain-Computer Interface

This paper presents a steady-state visual evoked potential (SSVEP) based online brain-computer interface (BCI) chatting system. In this application, it helps disable subjects to chat by means of their brain signal activities. Stimuli illuminated at different frequencies are displayed on liquid crystal display (LCD) screen. The BCI system implements a chatting interface programmed in Java, and it provides a convenient and high efficient communication platform for paralyzed patients. A novel idle state detection is introduced in this proposed system, and a high transfer rate of 63.54 bits/min and 92% idle state detection accuracy are achieved.

Chaozheng Li

Papers

An online SSVEP-based chatting system