SSVEP-Based BCI in a Smart Home Scenario

Saboor, Abdul; Rezeika, Aya; Stawicki, Piotr; Gembler, Felix; Grunenberg, Thomas; Volosyak, Ivan

doi:10.1007/978-3-319-59147-6_41

Cited by 20 publications

(3 citation statements)

References 18 publications

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“…SSVEP-based BCI offers people the possibility to recognize various commands and control corresponding devices in their houses by watching different stimuli [33]. By means of the quick response technology QR code, Abdul et al [34] designed an augmented reality smart glasses to control items in the environment, such as lights, coffee machines and elevators, by focusing on different SSVEP stimuli displayed on the glasses. Similarly, based on SSVEP-based BCI technology, a hand-free control smart home has been created in [35], which can control six devices.…”

Section: Healthcare Applicationsmentioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

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Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signalto-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.

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Section: Healthcare Applicationsmentioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

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“…Non-clinically, BCIs have been used by able-bodied users to control drones [58], smart homes [126], and augmented reality gaming [53]. In theory, non-invasive BCIs hold tremendous potential for human augmentation and alleviation Fig.…”

Section: Brain Computer Interfacesmentioning

confidence: 99%

Interfacing with the nervous system: a review of current bioelectric technologies

et al. 2017

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The aim of this study is to discuss the state of the art with regard to established or promising bioelectric therapies meant to alter or control neurologic function. We present recent reports on bioelectric technologies that interface with the nervous system at three potential sites-(1) the end organ, (2) the peripheral nervous system, and (3) the central nervous system-while exploring practical and clinical considerations.

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“…Because they require little to no voluntary motor control, BCIs can help people with severe motor deficits (e.g., locked-in syndrome) to communicate [ 2 ]. They may also have applications for healthy people [ 3 ]–[ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs

Habibzadeh

Norton

Vaughan

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction methods and channel selections to infer the SSVEP and relies on majority voting to pick the most likely target. The classifier extends the window length dynamically if no target obtains the majority of votes. Compared with existing solutions, our classifier: (i) does not assume that any single feature extraction method will consistently outperform the others; (ii) adapts the channel selection to individual users or tasks; (iii) uses dynamic window lengths; (iv) is unsupervised (i.e., does not need training). Collectively, these characteristics make the classifier easy-to-use, especially for caregivers and others with limited technical expertise. We evaluated the performance of our classifier on a publicly available benchmark dataset from 35 healthy participants. We compared the information transfer rate (ITR) of this new classifier to those of the minimum energy combination (MEC), maximum synchronization index (MSI), and filter bank canonical correlation analysis (FBCCA). The new classifier increases average ITR to 123.5 bits-per-minute (bpm), 47.5, 51.2, and 19.5 bpm greater than the MEC, MSI, and FBCCA classifiers, respectively.

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SSVEP-Based BCI in a Smart Home Scenario

Cited by 20 publications

References 18 publications

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

Interfacing with the nervous system: a review of current bioelectric technologies

A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs

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