A dataset of EEG signals from a single-channel SSVEP-based brain computer interface

Trinchese, P.; Vitiello, Autilia

doi:10.1016/j.dib.2021.106826

Cited by 15 publications

(11 citation statements)

References 7 publications

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“…Visual stimuli have been reported to consist of four alternating black and white 80 × 80 pixel squares on the black background with 60 Hz refreshing rate of the monitor. The dataset is named "A dataset of EEG signals from a singlechannel SSVEP-based brain-computer interface" and can be accessed over this reference [8].…”

Section: Experimental Setup and Mainframe Of The Researchmentioning

confidence: 99%

“…In this study, the DWT was employed to separate the sub-bands of the recorded signals. The results were processed by the RNN-based deep learning algorithms over the raw signals, and the DWT-based signals showed very promising biometric classification, offering a single-channel SSVEP biometric approach using dry-sensor technology [8]. Unlike traditional BCI helmets, dry-sensor technology can greatly improve wearable comfort and therefore, can be used quite successfully in biometric person recognition, as one of the next-generation Internet of Things (IoT) applications [2,7,8].…”

Section: Min Et Al Studied Ssvep-based Identification Using the Neuro...mentioning

confidence: 99%

“…Moreover, channel numbers/multi-channel recordings limit the use of EEG signals in real-world biometric applications, especially in wearable technologies [5][6][7]. At this point, steady-state visually evoked potentials (SSVEPs), when evoked in low-frequency signals, can provide high intersubject variability and low intra-subject variability with a single-channel approach [2,8].…”

Section: Introductionmentioning

confidence: 99%

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The Single-Channel SSVEP-based Biometric Approach Using RNN-based Deep Models: A Systematic Comparison

Gorur

Eraslan

2022

Preprint

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Biometric technology is the branch of science dealing with identification and verification of individuals, focusing on physiological and behavioral traits. These traits can be reliable, permanent, unique, and capable of distinguishing one person from others. Fingerprints, the iris, and the face are conventionally used in biometric techniques involving ID cards and passwords. However, due to security threats and fraud, these static textures and approaches can be abused. Consequently, biometric studies based on electroencephalography (EEG) have received increasing attention because each individual has a dynamic and unique pattern. However, classic EEG-based biometrics have significant deficiencies, including noise-prone signals, gel-based electrodes, and the need for multi-training/multi-channel acquisition and high mental effort. In contrast, steady-state visually evoked potential (SSVEP)-based biometrics have the important advantages of low signal-to-noise ratio and untrained usage. The SSVEP signals also embrace a number of other advantages. First, the occipital lobe is the only suitable region for collecting SSVEP signals without obstruction. Second, because elicited dynamic brain responses are a natural subconscious activity, the omnipresent SSVEP signals are the most frequently emitted signals. Anyone can continuously look into flickering lights having distinct frequencies, such as cell phone flashes, without extra physical or mental effort. Few studies involving multi-channel/multi-trial SSVEP-based biometric research are available in the current literature. Moreover, there is a lack of research comparing them to the single-channel single-trial SSVEP-based biometric approach using dry electrode-implemented recurrent neural network (RNN)-based deep learning models. To the best of our knowledge, no prior work has proposed such a biometric comparison of the RNN-based deep learning models and different sized time-series data compiled over raw and discrete wavelet transform (DWT)-based SSVEP signals. By achieving up to 100% accuracy using 11 individuals, the biometric recognition results are promising. This single-channel SSVEP-based biometric approach using RNN-based deep learning models may offer low-cost, user-friendly, and reliable individual identification authentication, leading to significant application domains.

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Section: Experimental Setup and Mainframe Of The Researchmentioning

confidence: 99%

Section: Min Et Al Studied Ssvep-based Identification Using the Neuro...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Single-Channel SSVEP-based Biometric Approach Using RNN-based Deep Models: A Systematic Comparison

Gorur

Eraslan

2022

Preprint

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“…Overall cost of the setup is quite less as compared to other commercially available systems as described in Table 4. [34] but it requires dry electrode consisting of 12 pointy pinches to be placed at the back of the scalp which is limiting its use for locked in patients as they are not able to use the system at sitting position. The proposed system is based on open source hardware with low components cost and providing medium level of efficiency with wear-ability and comfort for locked-in patients.…”

Section: Experimental Sessionmentioning

confidence: 99%

A low cost SSVEP-EEG based human-computer-interaction system for completely locked-in patients

et al. 2021

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Human computer interaction (HCI) for completely locked-in patients is a very difficult task. Nowadays, information technology (IT) is becoming an essential part of human life. Patients with completely locked-in state are generally unable to facilitate themselves by these useful technological advancements. Hence, they cannot use modern IT gadgets and applications throughout the lifespan after disability. Advancements in brain computer interface (BCI) enable operating IT devices using brain signals specifically when a person is unable to interact with the devices in conventional manner due to cognitive motor disability. However, existing state-of-the-art application specific BCI devices are comparatively too expensive. This paper presents a research and development work that aims to design and develop a low-cost general purpose HCI system that can be used to operate computers and a general purpose control panel through brain signals. The system is based on steady state visual evoked potentials (SSVEP). In proposed system, these electrical signals are obtained in response of a number of different flickering lights of different frequencies through electroencephalogram (EEG) electrodes and an open source BCI hardware. Successful trails conducted on healthy participants suggest that severely paralyzed subjects can operate a computer or control panel as an alternative to conventional HCI device.

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“…Acampora et al provide a single-channel EEG device in [15] and an expansion for SSVEP decoding in [14]. This device achieved a maximum SSVEP classification accuracy of 74.5% for a 2 s recording window, up to 92.7% over a 4 s window and 97.6% for a 6 s window for four SSVEP frequencies, which is state of the art.…”

Section: Introductionmentioning

confidence: 99%

Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding

Teversham

Wong

Hsieh

et al. 2022

Preprint

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This study details the development of a novel, approx. 20 pound (GBP) electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer a financially and operationally accessible device that can be deployed on a mass scale to facilitate education and public engagement in the domain of EEG sensing and neurotechnologies. Real-time decoding of steady-state visual evoked potentials (SSVEPs) is achieved using variations of the widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA and generalised CCA. All BCI functionality is executed on board an inexpensive ESP32 microcontroller. SSVEP decoding accuracy of 95.56 ± 3.74% with an ITR of 102 bits/min was achieved with modest calibration.

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A dataset of EEG signals from a single-channel SSVEP-based brain computer interface

Cited by 15 publications

References 7 publications

The Single-Channel SSVEP-based Biometric Approach Using RNN-based Deep Models: A Systematic Comparison

The Single-Channel SSVEP-based Biometric Approach Using RNN-based Deep Models: A Systematic Comparison

A low cost SSVEP-EEG based human-computer-interaction system for completely locked-in patients

Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding

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