Frequency Recognition in Ssvep-Based Bci Using Multiset Canonical Correlation Analysis

Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

doi:10.1142/s0129065714500130

Cited by 331 publications

(222 citation statements)

References 60 publications

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“…Recently, individual calibration data have been incorporated into target identification approaches to improve the performance of SSVEP-based BCIs (29)(30)(31)(32). By incorporating individual difference of SSVEPs in target identification, these methods all achieved significantly improved classification performance.…”

mentioning

confidence: 99%

High-speed spelling with a noninvasive brain–computer interface

Chen

Wang

Nakanishi

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

729

663

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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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mentioning

confidence: 99%

High-speed spelling with a noninvasive brain–computer interface

Chen

Wang

Nakanishi

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

729

663

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“…For comparison with the proposed approach, the CCA was selected as the baseline system [23]. For the mth stimulus frequency fm, a preconstructed reference signal set was formed using a series of sin-cosine waves, which can be written as follows:…”

Section: Comparison With Ccamentioning

confidence: 99%

Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System

Chen

Zaeni

et al. 2016

Applied Sciences

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Subjects with amyotrophic lateral sclerosis (ALS) consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI) application can effectively help subjects with ALS to participate in communication or entertainment. In this study, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system. To represent the characteristics of the measured electroencephalography (EEG) signals after visual stimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy tracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI system can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to attend a performance test of this BCI system. The canonical correlation analysis (CCA) was adopted to compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for proposed approach and CCA, respectively. The experimental results showed that the proposed approach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied in the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for entertainment purposes.

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“…One notable effort is Multiset CCA [13]. In this technique, the reference signal is not assumed to be a collection of sinusoids representing the stimulus frequencies and their harmonics.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for SSVEP Electroencephalography Based Brain–Computer Interfaces Using Learn++.NSE and Mutual Information

Sybeldon

Schmit

Akçakaya

2017

Entropy

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Brain-Computer Interfaces (BCI) using Steady-State Visual Evoked Potentials (SSVEP) are sometimes used by injured patients seeking to use a computer. Canonical Correlation Analysis (CCA) is seen as state-of-the-art for SSVEP BCI systems. However, this assumes that the user has full control over their covert attention, which may not be the case. This introduces high calibration requirements when using other machine learning techniques. These may be circumvented by using transfer learning to utilize data from other participants. This paper proposes a combination of ensemble learning via Learn++ for Nonstationary Environments (Learn++.NSE)and similarity measures such as mutual information to identify ensembles of pre-existing data that result in higher classification. Results show that this approach performed worse than CCA in participants with typical SSVEP responses, but outperformed CCA in participants whose SSVEP responses violated CCA assumptions. This indicates that similarity measures and Learn++.NSE can introduce a transfer learning mechanism to bring SSVEP system accessibility to users unable to control their covert attention.

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Frequency Recognition in Ssvep-Based Bci Using Multiset Canonical Correlation Analysis

Cited by 331 publications

References 60 publications

High-speed spelling with a noninvasive brain–computer interface

High-speed spelling with a noninvasive brain–computer interface

Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System

Transfer Learning for SSVEP Electroencephalography Based Brain–Computer Interfaces Using Learn++.NSE and Mutual Information

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