The past 20 years have witnessed unprecedented progress in braincomputer 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.brain-computer interface | electroencephalogram | steady-state visual evoked potentials | joint frequency-phase modulation B rain-computer interfaces (BCIs), which can provide a new communication channel to humans, have received increasing attention in recent years (1, 2). Among various applications, BCI spellers (3-9) are especially valuable because they can help patients with severe motor disabilities (e.g., amyotrophic lateral sclerosis, stroke, and spinal cord injury) communicate with other people. Currently, electroencephalogram (EEG) is the most popular method of implementing BCI spellers due to its noninvasiveness, simple operation, and relatively low cost. However, low signal-to-noise ratio (SNR) of the scalp-recorded EEG signals and lack of computationally efficient solutions in EEG modeling limit the information transfer rates (ITRs) of EEGbased BCI spellers to ∼1.0 bits per second (bps) (1, 4). For example, the well-known P300 speller proposed by Farwell and Donchin (5) can spell up to five letters per minute (∼0.5 bps). Until recently few studies using visual evoked potentials (VEPs) demonstrated higher ITRs of 1.7-2.4 bps (6, 7). In contrast, the invasive BCI spellers in humans and monkeys show higher performance. For example, the P300 speller with electrocorticogram recordings obtained a peak ITR of 1.9 bps in a human subject (8). A recent monkey study on keyboard neural prosthesis using multineuron recordings reported an ITR up to 3.5 bps (9). Although communication speed of the EEG-based spellers has been significantly improved in the past decade (4), it still remains a key obstacle to real-life applications in humans.Recently, the BCI speller using steady-state VEPs (SSVEPs) has attracted increasing attention due to its high communication rate and little user training (4, 10, 11). An SSVEP speller typically uses SSVEPs to detect the user's gaze direction to a target character (10). Although the SSVE...

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The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.

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.

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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 . 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 fromhttp://bci.med.tsinghua.edu.cn/download.html.

Implementing a complex spelling program using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) remains a challenge due to difficulties in stimulus presentation and target identification. This study aims to explore the feasibility of mixed frequency and phase coding in building a high-speed SSVEP speller with a computer monitor. A frequency and phase approximation approach was developed to eliminate the limitation of the number of targets caused by the monitor refresh rate, resulting in a speller comprising 32 flickers specified by eight frequencies (8-15 Hz with a 1 Hz interval) and four phases (0°, 90°, 180°, and 270°). A multi-channel approach incorporating Canonical Correlation Analysis (CCA) and SSVEP training data was proposed for target identification. In a simulated online experiment, at a spelling rate of 40 characters per minute, the system obtained an averaged information transfer rate (ITR) of 166.91 bits/min across 13 subjects with a maximum individual ITR of 192.26 bits/min, the highest ITR ever reported in electroencephalogram (EEG)-based BCIs. The results of this study demonstrate great potential of a high-speed SSVEP-based BCI in real-life applications.

Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard CCA method, which uses sinusoidal signals as reference signals, was first proposed for SSVEP detection without calibration. However, the detection performance can be deteriorated by the interference from the spontaneous EEG activities. Recently, various extended methods have been developed to incorporate individual EEG calibration data in CCA to improve the detection performance. Although advantages of the extended CCA methods have been demonstrated in separate studies, a comprehensive comparison between these methods is still missing. This study performed a comparison of the existing CCA-based SSVEP detection methods using a 12-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment. Classification accuracy and information transfer rate (ITR) were used for performance evaluation. The results suggest that individual calibration data can significantly improve the detection performance. Furthermore, the results showed that the combination method based on the standard CCA and the individual template based CCA (IT-CCA) achieved the highest performance.