A brain–computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain ...signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.
Classical brain–computer interface (BCI) systems are moving beyond lab demonstrations to real world applications with the development of associated hardware and software.Brain–computer interaction systems open up a wide range of BCI applications, especially in neural rehabilitation and human cognitive augmentation.Brain–computer intelligence systems reveal promising technologies for a new generation of artificial intelligence (AI) as well as a new generation of BCIs.
This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with a 40-target brain- computer interface (BCI) speller. The dataset consists of 64-channel ...Electroencephalogram (EEG) data from 35 healthy subjects (8 experienced and 27 naïve) while they performed a cue-guided target selecting task. The virtual keyboard of the speller was composed of 40 visual flickers, which were coded using a joint frequency and phase modulation (JFPM) approach. The stimulation frequencies ranged from 8 Hz to 15.8 Hz with an interval of 0.2 Hz. The phase difference between two adjacent frequencies was 0.5π. For each subject, the data included six blocks of 40 trials corresponding to all 40 flickers indicated by a visual cue in a random order. The stimulation duration in each trial was five seconds. The dataset can be used as a benchmark dataset to compare the methods for stimulus coding and target identification in SSVEP-based BCIs. Through offline simulation, the dataset can be used to design new system diagrams and evaluate their BCI performance without collecting any new data. The dataset also provides high-quality data for computational modeling of SSVEPs. The dataset is freely available from http://bci.med.tsinghua.edu.cn/download.html.
Over the past several decades, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have attracted attention from researchers in the field of neuroscience, neural engineering, and ...clinical rehabilitation. While the performance of BCI systems has improved, they do not yet support widespread usage. Recently, visual and auditory BCI systems have become popular because of their high communication speeds, little user training, and low user variation. However, building robust and practical BCI systems from physiological and technical knowledge of neural modulation of visual and auditory brain responses remains a challenging problem. In this paper, we review the current state and future challenges of visual and auditory BCI systems. First, we describe a new taxonomy based on the multiple access methods used in telecommunication systems. Then, we discuss the challenges of translating current technology into real-life practices and outline potential avenues to address them. Specifically, this review aims to provide useful guidelines for exploring new paradigms and methodologies to improve the current visual and auditory BCI technology.
Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to ...extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method
Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to ...extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used FFT (fast Fourier transform)-based spectrum estimation method
Due to the limited perceptual field, convolutional neural networks (CNN) only extract local temporal features and may fail to capture long-term dependencies for EEG decoding. In this paper, we ...propose a compact Convolutional Transformer, named EEG Conformer, to encapsulate local and global features in a unified EEG classification framework. Specifically, the convolution module learns the low-level local features throughout the one-dimensional temporal and spatial convolution layers. The self-attention module is straightforwardly connected to extract the global correlation within the local temporal features. Subsequently, the simple classifier module based on fully-connected layers is followed to predict the categories for EEG signals. To enhance interpretability, we also devise a visualization strategy to project the class activation mapping onto the brain topography. Finally, we have conducted extensive experiments to evaluate our method on three public datasets in EEG-based motor imagery and emotion recognition paradigms. The experimental results show that our method achieves state-of-the-art performance and has great potential to be a new baseline for general EEG decoding. The code has been released in https://github.com/eeyhsong/EEG-Conformer .
The past 20 years have witnessed unprecedented progress in brain–computer interfaces (BCIs). However, low communication rates remain key obstacles to BCI-based communication in humans. This study ...presents an electroencephalogram-based BCI speller that can achieve information transfer rates (ITRs) up to 5.32 bits per second, the highest ITRs reported in BCI spellers using either noninvasive or invasive methods. Based on extremely high consistency of frequency and phase observed between visual flickering signals and the elicited single-trial steady-state visual evoked potentials, this study developed a synchronous modulation and demodulation paradigm to implement the speller. Specifically, this study proposed a new joint frequency-phase modulation method to tag 40 characters with 0.5-s-long flickering signals and developed a user-specific target identification algorithm using individual calibration data. The speller achieved high ITRs in online spelling tasks. This study demonstrates that BCIs can provide a truly naturalistic high-speed communication channel using noninvasively recorded brain activities.
Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and ...simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established.
This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects.
The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ∼33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min(-1).
By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.
Recent advances in robotics, neuroscience, and signal processing make it possible to operate a robot through electroencephalography (EEG)-based brain-computer interface (BCI). Although some ...successful attempts have been made in recent years, the practicality of the entire system still has much room for improvement. The present study designed and realized a robotic arm control system by combing augmented reality (AR), computer vision, and steady-state visual evoked potential (SSVEP)-BCI. AR environment was implemented by a Microsoft HoloLens. Flickering stimuli for eliciting SSVEPs were presented on the HoloLens, which allowed users to see both the robotic arm and the user interface of the BCI. Thus users did not need to switch attention between the visual stimulator and the robotic arm. A four-command SSVEP-BCI was built for users to choose the specific object to be operated by the robotic arm. Once an object was selected, the computer vision would provide the location and color of the object in the workspace. Subsequently, the object was autonomously picked up and placed by the robotic arm. According to the online results obtained from twelve participants, the mean classification accuracy of the proposed system was 93.96 ± 5.05%. Moreover, all subjects could utilize the proposed system to successfully pick and place objects in a specific order. These results demonstrated the potential of combining AR-BCI and computer vision to control robotic arms, which is expected to further promote the practicality of BCI-controlled robots.
Objective: 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.
PDF
