Exactly Periodic Spatial Filter for SSVEP Based BCIs
01 Oct 2018-pp 237-242
TL;DR: A novel, high accuracy, calibration less spatial filter for reliable steady-state visual evoked potential (SSVEP) extraction from noisy electroencephalogram (EEG) data called exactly periodic subspace decomposition (EPSD).
Abstract: This study introduces a novel, high accuracy, calibration less spatial filter for reliable steady-state visual evoked potential (SSVEP) extraction from noisy electroencephalogram (EEG) data. The proposed method, exactly periodic subspace decomposition (EPSD), utilises the periodic properties of the SSVEP components to achieve a robust spatial filter for SSVEP extraction. It tries to extract the SSVEP components by projecting the EEG data onto a subspace where only the target signal components are retained. The performance of the method was tested on an SSVEP dataset obtained from ten subjects and compared with common SSVEP spatial filtering and detection techniques. The results obtained from the study shows that EPSD consistently provides a significant improvement in detection performance than other SSVEP spatial filters used in brain-computer interface (BCI) applications.
References
More filters
TL;DR: Novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented, tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates.
Abstract: In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed
511 citations
"Exactly Periodic Spatial Filter for..." refers background or methods in this paper
...The aim here is to find the linear combination of the channels that maximizes the signal to noise ratio of the SSVEP signals [9], [18]....
[...]
...1) Minimum energy combination (MEC): MEC algorithm is designed in [9] to obtain the linear combination of channels that decreases the noise in the EEG signals....
[...]
...In MEC and MCC, the target detection is achieved using normalized PSDA of the filtered EEG signals which employs extensive modeling of the background EEG [9]....
[...]
TL;DR: A comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects validated the efficiency of the proposal.
Abstract: Objective: This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller. Methods: Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies. This study conducted a comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects. An online BCI speller was further implemented using a cue-guided target selection task with 20 subjects and a free-spelling task with 10 of the subjects. Results: The offline comparison results indicate that the proposed TRCA-based approach can significantly improve the classification accuracy compared with the extended CCA-based method. Furthermore, the online BCI speller achieved averaged information transfer rates (ITRs) of 325.33 ± 38.17 bits/min with the cue-guided task and 198.67 ± 50.48 bits/min with the free-spelling task. Conclusion: This study validated the efficiency of the proposed TRCA-based method in implementing a high-speed SSVEP-based BCI. Significance: The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.
455 citations
"Exactly Periodic Spatial Filter for..." refers background or methods in this paper
...In SSVEP based BCIs, the aim of spatial filtering is to improve the efficiency of SSVEP detection by improving the signal to noise ratio of SSVEP components in the acquired EEG....
[...]
...SSVEP based BCIs have many advantages such as negligible training and high information transfer rates (ITR) compared to other BCIs [4]....
[...]
...05) corrected) were conducted to analyse the differences between the factors more conclusively [4]....
[...]
...Commonly used spatial filtering methods in SSVEP BCIs include best bipolar combination (BCC), principal component analysis (PCA), minimum energy combination (MEC), maximum contrast combination (MCC), and partial least squares (PLS) spatial filter [8]....
[...]
...Electroencephalogram (EEG) based BCIs are an emerging modality, due to their comparative low cost, temporal resolution, portability and lack of risks [2]....
[...]
TL;DR: The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed.
Abstract: This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP) generated in response to phase-reversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.
442 citations
"Exactly Periodic Spatial Filter for..." refers background in this paper
...SSVEP components are generated in response to flickering targets at frequencies greater than 4 Hz [5] and their amplitude is influenced by the visual spatial attention provided by the gazing user [6]....
[...]
TL;DR: The current status and future prospects of BCI technology and its clinical applications are discussed, BCI is defined, the BCI-relevant signals from the human brain are reviewed, and the functional components of BCIs are described.
Abstract: Braincomputer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development has grown explosively over the past two decades. Efforts have begun recently to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this paper, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology and identify potential users and potential applications. Lastly, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.
439 citations
"Exactly Periodic Spatial Filter for..." refers background in this paper
...provide useful information regarding the response of the user to imagined motor function or to an sensory stimulus [3]....
[...]
TL;DR: The results suggest that individual calibration data can significantly improve the detection performance of SSVEP detection methods and show that the combination method based on the standard CCA and the individual template based CCA (IT-CCA) achieved the highest performance.
Abstract: Canonical correlation analysis (CCA) has been widely used in the detection of the steady-state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard CCA method, which uses sinusoidal signals as reference signals, was first proposed for SSVEP detection without calibration. However, the detection performance can be deteriorated by the interference from the spontaneous EEG activities. Recently, various extended methods have been developed to incorporate individual EEG calibration data in CCA to improve the detection performance. Although advantages of the extended CCA methods have been demonstrated in separate studies, a comprehensive comparison between these methods is still missing. This study performed a comparison of the existing CCA-based SSVEP detection methods using a 12-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment. Classification accuracy and information transfer rate (ITR) were used for performance evaluation. The results suggest that individual calibration data can significantly improve the detection performance. Furthermore, the results showed that the combination method based on the standard CCA and the individual template based CCA (IT-CCA) achieved the highest performance.
241 citations
"Exactly Periodic Spatial Filter for..." refers methods in this paper
...To overcome this, recent SSVEP detection methods such as Multiway CCA, Multiset CCA and IT-CCA [11], [12] rely on calibration data to obtain a reliable estimate of the SSVEP components to be used as robust references....
[...]