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.

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.

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.

Information Transfer Rate of Nonspiking Afferent Neurons in the Crab

2004 ◽  
Vol 92 (1) ◽  
pp. 302-310 ◽  
Author(s):  
Ralph A. DiCaprio
Keyword(s):  
Membrane Potential ◽  
Transfer Rate ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Major Effect ◽  
Information Rate ◽  
Afferent Neurons ◽  
Cable Properties ◽  
Information Transfer Rate ◽  
Consequent Reduction

The thoracic-coxal muscle receptor organ (TCMRO) is the only proprioceptor at the thoracic-coxal joint in the crab leg. The S and T afferent neurons of the TCMRO convey signals to the CNS solely by means of graded changes in membrane potential. The rate of information transfer of these afferents was determined by measuring the signal-to-noise ratio (SΝR) of these cells after repeated stimulation of the receptor with identical sequences of random movement and applying the Shannon formula for the information capacity of a Gaussian channel. Intracellular recordings were made from the S and T afferents adjacent to the transduction site at the origin of the receptor and along the axon 5–7 mm distal to this site. These nonspiking afferents transduce receptor movement and transmit this information with extremely high fidelity. The SNR of both neurons near the transduction site was >1000 over most of the 200 Hz stimulation bandwidth, and the mean information transfer rate was ∼2,500 bits/s. When calculated over a wider bandwidth of 500 Hz, the information rate was >4,600 bits/s. The effect of axonal cable properties on the information rate was evaluated by determining the SNR from membrane potential recordings made 5–7 mm distal to the transduction region. The major effect of graded transmission along the axon was attenuation and low-pass filtering of the sensory signal. The consequent reduction in signal power and bandwidth decreased the information transfer by ∼10–15% over 200 Hz and ∼30% over a 500 Hz bandwidth.

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