Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs

Carvalho, Sarah N.; Costa, Tássia R.; Uribe, Luisa Fernanda Suárez; Soriano, Diogo C.; Yared, Glauco F. G.; Coradine, Luis Cláudius; Attux, Romis

doi:10.1016/j.bspc.2015.05.008

Cited by 74 publications

(63 citation statements)

References 25 publications

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“…The synchrostates refer to the phase‐synchronized states which are stable for a few milliseconds when a face perception task is executed (Jamal et al, 2013). Spectrogram refers to the squared magnitude of STFT and gives information regarding power content in the signal (Carvalho et al, 2015).…”

Section: Methodologiesmentioning

confidence: 99%

Event related potential analysis techniques for autism spectrum disorders: A review

Sheela

Puthankattil²

2018

Intl J of Devlp Neuroscience

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Autism Spectrum Disorders (ASD) comprise all pervasive neurodevelopmental diseases marked by deficits in social and communication skills, delayed cognitive development, restricted and repetitive behaviors. The core symptoms begin in early childhood, may continue life-long resulting in poor performance in adult stage. Event-related potential (ERP) is basically a time-locked electroencephalogram signal elicited by various stimuli, related to sensory and cognitive processes. The various ERP based techniques used for the study of ASD are considered in this review. ERP based study offers the advantage of being a non-invasive technique to measure the brain activity precisely. The techniques are categorized into three based on the processing domain: time, frequency and time-frequency. Power spectral density, coherence, phase synchrony, multiscale entropy, modified multiscale entropy, sum of signed differences, synchrostates and variance are some of the measures that have been widely used to study the abnormalities in frequency bands and brain connectivity. Various signal processing techniques such as Fast Fourier Transform, Discrete Fourier Transform, Short-Time Fourier Transform, Principal Component Analysis, Wavelet Transform, Directed Transfer Function etc. have been used to analyze the recorded signals so as to unravel the distinctive event-related potential patterns in individuals with ASD. The review concludes that ERP proves to be an efficient tool in detecting the brain abnormalities and connectivity issues, indicating the heterogeneity of ASD. Many advanced techniques are utilized to decipher the underlying neural circuitry so as to aid in therapeutic interventions for improving the core areas of deficits.

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Section: Methodologiesmentioning

confidence: 99%

Event related potential analysis techniques for autism spectrum disorders: A review

Sheela

Puthankattil²

2018

Intl J of Devlp Neuroscience

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“…CCA is a method for the extraction of similarities between two data sets [34,87]. CCA was first used in BCI studies by Lin et al to detect SSVEP frequencies [88].…”

Section: Ssvep Detectionmentioning

confidence: 99%

A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

Mannan

Kamran

Kang

et al. 2020

Sensors

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Steady-state visual evoked potentials (SSVEPs) have been extensively utilized to develop brain–computer interfaces (BCIs) due to the advantages of robustness, large number of commands, high classification accuracies, and information transfer rates (ITRs). However, the use of several simultaneous flickering stimuli often causes high levels of user discomfort, tiredness, annoyingness, and fatigue. Here we propose to design a stimuli-responsive hybrid speller by using electroencephalography (EEG) and video-based eye-tracking to increase user comfortability levels when presented with large numbers of simultaneously flickering stimuli. Interestingly, a canonical correlation analysis (CCA)-based framework was useful to identify target frequency with a 1 s duration of flickering signal. Our proposed BCI-speller uses only six frequencies to classify forty-eight targets, thus achieve greatly increased ITR, whereas basic SSVEP BCI-spellers use an equal number of frequencies to the number of targets. Using this speller, we obtained an average classification accuracy of 90.35 ± 3.597% with an average ITR of 184.06 ± 12.761 bits per minute in a cued-spelling task and an ITR of 190.73 ± 17.849 bits per minute in a free-spelling task. Consequently, our proposed speller is superior to the other spellers in terms of targets classified, classification accuracy, and ITR, while producing less fatigue, annoyingness, tiredness and discomfort. Together, our proposed hybrid eye tracking and SSVEP BCI-based system will ultimately enable a truly high-speed communication channel.

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“…Currently, two of the most popular methods for SSVEP feature extraction are: canonical correlation analysis (CCA, [17], [18], [19], [20], [21]), which calculates the correlation between the user's EEG signal and the target frequencies, and power spectral density analysis (PSDA, [22], [23]), which uses frequency components of the EEG signal as features for classification. CCA has a high accuracy and does not need training data.…”

Section: Introductionmentioning

confidence: 99%

Improving SSVEP-BCI performance using pre-trial normalization methods

Henshaw

Liu

Romano

2017

2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)

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Abstract-A brain-computer interface (BCI) enables users to communicate through a computer using only their brain signals, by extracting brain signal features containing information representative of the user's intent, and can be used in a wide variety of areas such as entertainment, rehabilitation, or assistive technologies. In this paper, two novel normalization methods are assessed with the aim of improving the quality of the extracted features: Baseline-Corrected canonical correlation analysis (BC-CCA), and Scaled CCA. Both methods are found to be able to improve classification accuracy in conditions using frequencies with a large range, whilst BC-CCA is the superior of the two, improving SSVEP detection accuracy by as much as 9.22%.

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Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs

Cited by 74 publications

References 25 publications

Event related potential analysis techniques for autism spectrum disorders: A review

Event related potential analysis techniques for autism spectrum disorders: A review

A Hybrid Speller Design Using Eye Tracking and SSVEP Brain–Computer Interface

Improving SSVEP-BCI performance using pre-trial normalization methods

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