A novel BCI-SSVEP based approach for control of walking in Virtual Environment using a Convolutional Neural Network

Bevilacqua, Vitoantonio; Tattoli, Giacomo; Buongiorno, Domenico; Loconsole, Claudio; Leonardis, Daniele; Barsotti, Michele; Frisoli, Antonio; Bergamasco, Massimo

doi:10.1109/ijcnn.2014.6889955

Cited by 43 publications

(23 citation statements)

References 28 publications

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“…sequence, previous attempts at applying CNNs to SSVEP classification have used domain-specific representations to reduce the amount of training data required [36][37][38]. These approaches utilize the fast-fourier transform (FFT) in their deep-learning models, thereby transforming EEG signals from the time-domain to the frequency-domain.…”

Section: Introductionmentioning

confidence: 99%

Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials

et al. 2018

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Objective. Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (EEG) and are commonly used in brain-computer interfaces (BCIs). However, methods for high-accuracy decoding of SSVEPs usually require hand-crafted approaches that leverage domain-specific knowledge of the stimulus signals, such as specific temporal frequencies in the visual stimuli and their relative spatial arrangement. When this knowledge is unavailable, such as when SSVEP signals are acquired asynchronously, such approaches tend to fail. Approach. In this paper, we show how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for user-specific calibration. Main results. The Compact-CNN demonstrates across subject mean accuracy of approximately 80 %, out-performing current state-of-the-art, handcrafted approaches using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, the Compact-CNN approach can reveal the underlying feature representation, revealing that the deep learner extracts additional phase-and amplitude-related features associated with the structure of the dataset. Significance. We discuss how our Compact-CNN shows promise for BCI applications that allow users to freely gaze/attend to any stimulus at any time (e.g., asynchronous BCI) as well as provides a method for analyzing SSVEP signals in a way that might augment our understanding about the basic processing in the visual cortex.Evoked potentials are robust signals in the electroencephalogram (EEG) induced by sensory stimuli, and they have been used to study normal and abnormal function of the sensory cortex [1]. The most well-studied of these are Steady-State Visual Evoked Potentials (SSVEPs), which are neural oscillations in the visual cortex that are evoked from stimuli that temporally flicker in a narrow frequency band [2,3]. SSVEPs likely arise from a reorganization of spontaneous intrinsic brain oscillations in response to a stimulus [4]. Paradigms leveraging SSVEP responses have been used to investigate the organization of the visual system [5,6], identify biomarkers of disease and sensory function [7][8][9], and probe visual perception [10,11].The robustness of SSVEP has enabled its use as a control signal for brain computer interfaces (BCIs) that enable low-bandwith communication for individuals with catastrophic loss of motor functions, bypassing neuro-muscular pathways and establishing a communication link directly to the brain [12,13]. In a typical SSVEP BCI, a patient/subject is presented with a grid of squares on a computer monitor, where each square contains semantic information such as a letter, number, character, or action. Superimposed on these squares are visual flicker frequencies that uniquely "tag" each square, thus mapp...

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Section: Introductionmentioning

confidence: 99%

Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials

et al. 2018

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“…• Sex: {Male, Female} • Age: {≥18} • Schooling Age: {[0-4], [5][6][7], [8][9][10][11][12], [13][14][15][16][17] years} The first four attributes are obtained by interviewing the patient, while the remaining two attributes are obtained after processing the EEG signals. All these records are input for a Self-Organizing Maps (SOM) [12] Neural Networks that search for the number of different classes by means the positions of their centroids.…”

Section: Methods and Data Of Clusteringmentioning

confidence: 99%

“…Moreover, in [6] a quantitative analysis for assessing driver's cognitive responses by investigating the neurobiological information provided by EEG in traffic -light experiments in a VR dynamic driving environment is presented. In [7], it is used a scenario designed in a Virtual Reality to evaluate the correlation between the age of a person with the ability of way finding, while in [8] Visual Stimuli in a VE are used to develop a Brain Computer Interface to navigate in a Virtual Environment.…”

Section: Introductionmentioning

confidence: 99%

A P300 Clustering of Mild Cognitive Impairment Patients Stimulated in an Immersive Virtual Reality Scenario

Bevilacqua

Brunetti

Biase

et al. 2015

Intelligent Computing Theories and Methodologies

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Abstract. In this paper, we present an innovative framework useful for clustering patients affected by a mild cognitive impairment and designed to improve the living environment and the lifestyle of patients in order to delay their cognitive state decline. The cognitive state changes are evaluated by means the event -related potentials elicited by environmental stimuli administered in several Virtual Reality scenarios. In particular, we formerly describe our innovative Virtual Reality environment, the protocol of stimuli administration, the procedure to measure the P300 latency in the response signal and finally the Self Organizing Map used to cluster the data. This research finds application in the fields of re-qualification of the environments for patients and healthcare introducing a new method for evaluation of best living conditions through VR.

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“…Among different applications, SSVEP based BCIs that allow users to explore virtual worlds also have received the attention of the research community. In Bevilacqua et al (2014), the authors propose an avatar control system (in first person view) which uses 12, 15 and 20 Hz stimulus frequencies for executing turn left, turn right and go forward commands, respectively. Additionally by Li et al (2017), the researcher propose a car driving system in a virtual world.…”

Section: Internet Of Thingsmentioning

confidence: 99%

Review of Steady State Visually Evoked Potential Brain-Computer Interface Applications: Technological Analysis and Classification

Zavala¹,

López²,

Chicaiza³

et al. 2019

J. Eng. Appl. Sci.

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Brain-computer interface is a technology which creates a new way of communication between a person's brain and the external world. To achieve this objective, the brainwaves of a person must be gathered by using specialized devices and then classified in different categories that are associated with specific commands. In the process of brainwave gathering, brain activities of a person can be influenced by different types of stimuli to get the desired results and one of the most important and popular stimuli used in this field is steady state visually evoked potential. Based on this background, this review seeks to show and analyze a series of articles that have been executed around the world related to brain-computer interface applications using steady state visually evoked potential. This review has been executed with the objective of identifying the advantages and limitations of utilizing steady state visually evoked potentials, its main areas of application and the future challenges. Additionally, this review analyzes the different technologies involved to the implementation of state visually evoked potential systems such as signal classification techniques, electroencephalography devices, channels, verification metrics and experimental environments used in the research projects. In summary, this review intends to guide the scientific community about the different aspects involved in conducting research on the development of brain-computer interface applications using electroencephalography devices and steady state visually evoked potential.

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A novel BCI-SSVEP based approach for control of walking in Virtual Environment using a Convolutional Neural Network

Cited by 43 publications

References 28 publications

Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials

Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials

A P300 Clustering of Mild Cognitive Impairment Patients Stimulated in an Immersive Virtual Reality Scenario

Review of Steady State Visually Evoked Potential Brain-Computer Interface Applications: Technological Analysis and Classification

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