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...

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.

In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain-computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically in relation to speed, user variation and ease of use. With these improvements in mind, this paper presents an online multi-channel SSVEP-based BCI system using a canonical correlation analysis (CCA) method for extraction of frequency information associated with the SSVEP. The key parameters, channel location, window length and the number of harmonics, are investigated using offline data, and the result used to guide the design of the online system. An SSVEP-based BCI system with six targets, which use nine channel locations in the occipital and parietal lobes, a window length of 2 s and the first harmonic, is used for online testing on 12 subjects. The results show that the proposed BCI system has a high performance, achieving an average accuracy of 95.3% and an information transfer rate of 58 +/- 9.6 bit min(-1). The positive characteristics of the proposed system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.

This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to > 90% of people with a high ITR in living environments.

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.