Multi-Channel SSVEP Pattern Recognition Based on MUSIC

2014 ◽  
Vol 539 ◽  
pp. 84-88 ◽  
Author(s):  
Kun Chen ◽  
Quan Liu ◽  
Qing Song Ai
Keyword(s):  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Frequency Resolution ◽  
Disabled People ◽  
Multiple Signal Classification ◽  
Computer Interfaces ◽  
Information Transfer Rate

Brain computer interfaces (BCIs) have become a research hotspot in recent years because of great potentials to help disabled people communicate with the outside world. Among different paradigms, steady state visual evoked potential (SSVEP)-based BCIs are commonly implemented in real applications, because they provide higher signal to noise ratio (SNR) and greater information transfer rate (ITR) than other BCI techniques. Various algorithms have been employed for SSVEP signal processing, like fast Fourier transform (FFT), wavelet analysis and canonical correlation analysis (CCA). In this paper, a new method based on multiple signal classification (MUSIC) was proposed for SSVEP feature extraction. The experimental results proved that it could provide higher frequency resolution and the recognition accuracy was excellent via adjusting some parameters.

SSVEP Recognition by Using Higher Harmonics Based on Music

2016 ◽  
Vol 16 (4) ◽  
Author(s):  
Kun Chen ◽  
Fei Xu ◽  
Quan Liu ◽  
Haojie Liu ◽  
Yang Zhang ◽  
...  
Keyword(s):  
Transfer Rate ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Signal To Noise Ratio ◽  
Signal Classification ◽  
Recognition Time ◽  
Multiple Signal Classification ◽  
Computer Interfaces ◽  
Information Transfer Rate

Among different brain–computer interfaces (BCIs), the steady-state visual evoked potential (SSVEP)-based BCI has been widely used because of its higher signal to noise ratio (SNR) and greater information transfer rate (ITR). In this paper, a method based on multiple signal classification (MUSIC) was proposed for multidimensional SSVEP signal processing. Both fundamental and second harmonics of SSVEPs were employed for the final target recognition. The experimental results proved it has the advantage of reducing recognition time. Also, the relation between the duty-cycle of the stimulus signals and the amplitude of the second harmonics of SSVEPs was discussed via experiments. In order to verify the feasibility of proposed methods, a two-layer spelling system was designed. Different subjects including those who have never used BCIs before used the system fluently in an unshielded environment.

scholarly journals In-Ear Electrode EEG for Practical SSVEP BCI

Technologies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 63
Author(s):  
Surej Mouli ◽  
Ramaswamy Palaniappan ◽  
Emmanuel Molefi ◽  
Ian McLoughlin
Keyword(s):  
Transfer Rate ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Stimulus Frequency ◽  
Alternative Methods ◽  
Occipital Region ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Information Transfer Rate

Steady State Visual Evoked Potential (SSVEP) methods for brain–computer interfaces (BCI) are popular due to higher information transfer rate and easier setup with minimal training, compared to alternative methods. With precisely generated visual stimulus frequency, it is possible to translate brain signals into external actions or signals. Traditionally, SSVEP data is collected from the occipital region using electrodes with or without gel, normally mounted on a head cap. In this experimental study, we develop an in-ear electrode to collect SSVEP data for four different flicker frequencies and compare against occipital scalp electrode data. Data from five participants demonstrates the feasibility of in-ear electrode based SSVEP, significantly enhancing the practicability of wearable BCI applications.

scholarly journals Steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) of Chinese speller for a patient with amyotrophic lateral sclerosis: A case report

2020 ◽  
Vol 08 (01) ◽  
pp. 40-52
Author(s):  
Nanlin Shi
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Steady State ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Information Transfer Rate ◽  
Lateral Sclerosis ◽  
Visual Evoked

This study applied a steady-state visual evoked potential (SSVEP) based brain–computer interface (BCI) to a patient in lock-in state with amyotrophic lateral sclerosis (ALS) and validated its feasibility for communication. The developed calibration-free and asynchronous spelling system provided a natural and efficient communication experience for the patient, achieving a maximum free-spelling accuracy above 90% and an information transfer rate of over 22.203 bits/min. A set of standard frequency scanning and task spelling data were also acquired to evaluate the patient’s SSVEP response and to facilitate further personalized BCI design. The results demonstrated that the proposed SSVEP-based BCI system was practical and efficient enough to provide daily life communication for ALS patients.

scholarly journals Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5309
Author(s):  
Akira Ikeda ◽  
Yoshikazu Washizawa
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Visual Evoked Potential ◽  
Evoked Potential ◽  
Extraction Methods ◽  
Stimulation Frequency ◽  
Event Related Potential ◽  
Spectrum Estimation ◽  
Complex Valued ◽  
Visual Evoked

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

scholarly journals The Validity of Steady-State Visual Evoked Potentials as Attention Tags and Input Signals: A Critical Perspective of Frequency Allocation and Number of Stimuli

2020 ◽  
Vol 10 (9) ◽  
pp. 616 ◽  
Author(s):  
Lu Wang ◽  
Dan Han ◽  
Binbin Qian ◽  
Zhenhao Zhang ◽  
Zhijun Zhang ◽  
...  
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Recognition Rate ◽  
Frequency Allocation ◽  
Cognitive Studies ◽  
Computer Interfaces ◽  
Information Transfer Rate ◽  
Input Signals ◽  
Specific Frequency ◽  
Visual Evoked

Steady-state visual evoked potential (SSVEP) is a periodic response to a repetitive visual stimulus at a specific frequency. Currently, SSVEP is widely treated as an attention tag in cognitive activities and is used as an input signal for brain–computer interfaces (BCIs). However, whether SSVEP can be used as a reliable indicator has been a controversial issue. We focused on the independence of SSVEP from frequency allocation and number of stimuli. First, a cue–target paradigm was adopted to examine the interaction between SSVEPs evoked by two stimuli with different frequency allocations under different attention conditions. Second, we explored whether signal strength and the performance of SSVEP-based BCIs were affected by the number of stimuli. The results revealed that no significant interaction of SSVEP responses appeared between attended and unattended stimuli under various frequency allocations, regardless of their appearance in the fundamental or second-order harmonic. The amplitude of SSVEP suffered no significant gain or loss under different numbers of stimuli, but the performance of SSVEP-based BCIs varied along with duration of stimuli; that is, the recognition rate was not affected by the number of stimuli when the duration of stimuli was long enough, while the information transfer rate (ITR) presented the opposite trend. It can be concluded that SSVEP is a reliable tool for marking and monitoring multiple stimuli simultaneously in cognitive studies, but much caution should be taken when choosing a suitable duration and the number of stimuli, in order to achieve optimal utility of BCIs in the future.

scholarly journals Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface

Micromachines ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 681
Author(s):  
Bor-Shyh Lin ◽  
Bor-Shing Lin ◽  
Tzu-Hsiang Yen ◽  
Chien-Chin Hsu ◽  
Yao-Chin Wang
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Signal Quality ◽  
Eeg Signal ◽  
Visually Evoked Potential ◽  
Information Transfer Rate ◽  
Field Programmable

Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min).

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