Periodic component analysis as a spatial filter for SSVEP-based brain–computer interface

Kumar, G R Kiran; Reddy, M. Ramasubba

doi:10.1016/j.jneumeth.2018.06.003

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…The classification accuracy of SSVEP-based BCI is closely related to the signal-to-noise ratio (SNR) (Zhang et al, 2014;Kumar and Reddy, 2018). With an increase in the window length, the SNR of the SSVEP components increases, thereby leading to a higher classification accuracy (Xing et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces

Kim

2021

Front. Neuroinform.

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There remains an active investigation on elevating the classification accuracy and information transfer rate of brain-computer interfaces based on steady-state visual evoked potential. However, it has often been ignored that the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can be affected through the minor displacement of the electrodes from their optimal locations in practical applications because of the mislocation of electrodes and/or concurrent use of electroencephalography (EEG) devices with external devices, such as virtual reality headsets. In this study, we evaluated the performance robustness of SSVEP-based BCIs with respect to the changes in electrode locations for various channel configurations and classification algorithms. Our experiments involved 21 participants, where EEG signals were recorded from the scalp electrodes densely attached to the occipital area of the participants. The classification accuracies for all the possible cases of electrode location shifts for various channel configurations (1–3 channels) were calculated using five training-free SSVEP classification algorithms, i.e., the canonical correlation analysis (CCA), extended CCA, filter bank CCA, multivariate synchronization index (MSI), and extended MSI (EMSI). Then, the performances of the BCIs were evaluated using two measures, i.e., the average classification accuracy (ACA) across the electrode shifts and robustness to the electrode shift (RES). Our results showed that the ACA increased with an increase in the number of channels regardless of the algorithm. However, the RES was enhanced with an increase in the number of channels only when MSI and EMSI were employed. While both ACA and RES values for the five algorithms were similar under the single-channel condition, both ACA and RES values for MSI and EMSI were higher than those of the other algorithms under the multichannel (i.e., two or three electrodes) conditions. In addition, EMSI outperformed MSI when comparing the ACA and RES values under the multichannel conditions. In conclusion, our results suggested that the use of multichannel configuration and employment of EMSI could make the performance of SSVEP-based BCIs more robust to the electrode shift from the optimal locations.

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Section: Discussionmentioning

confidence: 99%

Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces

Kim

2021

Front. Neuroinform.

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“…To extract the feature of the induced SSVEP signals, there have been developed several spatial filters to detect the peak frequency in the SSVEP. In this study, the standard canonical correlation analysis (CCA) method, which is famous for multichannel detection technique (Spüler et al, 2013;Kumar and Reddy, 2018), was applied to the segmented EEG data to obtain the peak frequency. The CCA method utilizes the projection vectors W x ∈ R N c and W y ∈ R 2N h as the spatial filters by the linear transformation of the segmented EEG data X ∈ R N c ×N s and a set of sinusoidal harmonics of each flickering frequency (Equation 1) for the reference signals Y k ∈ R 2N h ×N s , respectively.…”

Section: Eeg-bci Systemmentioning

confidence: 99%

EEG-controlled tele-grasping for undefined objects

Kim,

Choi,

Jang

et al. 2023

Front. Neurorobot.

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This paper presents a teleoperation system of robot grasping for undefined objects based on a real-time EEG (Electroencephalography) measurement and shared autonomy. When grasping an undefined object in an unstructured environment, real-time human decision is necessary since fully autonomous grasping may not handle uncertain situations. The proposed system allows involvement of a wide range of human decisions throughout the entire grasping procedure, including 3D movement of the gripper, selecting proper grasping posture, and adjusting the amount of grip force. These multiple decision-making procedures of the human operator have been implemented with six flickering blocks for steady-state visually evoked potentials (SSVEP) by dividing the grasping task into predefined substeps. Each substep consists of approaching the object, selecting posture and grip force, grasping, transporting to the desired position, and releasing. The graphical user interface (GUI) displays the current substep and simple symbols beside each flickering block for quick understanding. The tele-grasping of various objects by using real-time human decisions of selecting among four possible postures and three levels of grip force has been demonstrated. This system can be adapted to other sequential EEG-controlled teleoperation tasks that require complex human decisions.

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“…This approach is not robust and feasible for real-time BCI since the characteristics of SSVEP, such as magnitude distribution over the scalp and frequency response, tend to exhibit huge variations across subjects (Allison et al 2010). To overcome these drawbacks, a number of multichannel spatial filtering techniques that exploit the spatial redundancy have been proposed that try to find a linear combination of EEG electrodes based on the SSVEP signal model to enhance the SNR of the SSVEP response (Abu-Alqumsan and Peer 2016, Kumar and Reddy 2018). Although these trainingfree methods provide satisfactory detection performance at large data lengths (>3 s), they fail at shorter data lengths since SSVEP components are highly subject-specific and vary not only across subjects but also within a given recording session due to a number of factors (Krauledat et al 2008).…”

Section: Journal Of Neural Engineeringmentioning

confidence: 99%

Latent common source extraction via a generalized canonical correlation framework for frequency recognition in SSVEP based brain–computer interfaces

Kumar

Reddy

2019

J. Neural Eng.

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Objective. This study introduces and evaluates a novel target identification method, latent common source extraction (LCSE), that uses subject-specific training data for the enhancement of detection of steady-state visual evoked potential (SSVEP). Approach. LCSE seeks to construct a common latent representation of the SSVEP signal subspace that is stable across multiple trials of electroencephalographic (EEG) data. The spatial filter thus obtained improves the signal-to-noise ratio (SNR) of the SSVEP components by removing nuisance signals that are irrelevant to the generalized signal representation learnt from the given data. In this study a comparison of SSVEP identification performance between the proposed method, extended canonical correlation analysis (ExtCCA) and multiset canonical correlation analysis (MsetCCA) was conducted using SSVEP benchmark data of 40 targets recorded from 35 subjects to validate the effectiveness of the LCSE framework. Main results. The results indicate that the LCSE framework significantly outperforms the other two methods in terms of both classification accuracy and information transfer rates (ITRs). Significance. The significant improvement in the target identification performance demonstrates that the proposed LCSE method can be seen as a promising potential candidate for efficient SSVEP detection in brain-computer interface (BCI) systems.

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Periodic component analysis as a spatial filter for SSVEP-based brain–computer interface

Cited by 7 publications

References 31 publications

Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces

Influence of the Number of Channels and Classification Algorithm on the Performance Robustness to Electrode Shift in Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces

EEG-controlled tele-grasping for undefined objects

Latent common source extraction via a generalized canonical correlation framework for frequency recognition in SSVEP based brain–computer interfaces

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