Abstract
Brain-computer interfaces (BCIs) and their associated technologies have the potential to shape future forms of communication, control, and security. Specifically, the steady-state visual evoked potential (SSVEP) based BCIs have the advantages of better recognition accuracy, and higher information transfer rate (ITR) compared to other BCI modalities. To fully exploit the capabilities of such devices, it is necessary to understand the underlying biological features of SSVEPs and design the system considering their inherent characteristics. This paper introduces bio-inspired filter banks (BIFBs) for improved SSVEP frequency recognition. SSVEPs are frequency selective, subject-specific, and their power gets weaker as the frequency of the visual stimuli increases. Therefore, the gain and bandwidth of the filters are designed and tuned based on these characteristics while also incorporating harmonic SSVEP responses. The BIFBs are utilized in the feature extraction stage to increase the separability of classes. This method not only improves the recognition accuracy but also increases the total number of available commands in a BCI system by allowing the use of stimuli frequencies that elicit weak SSVEP responses. The BIFBs are promising particularly in the high-frequency band, which causes less visual fatigue. Hence, the proposed approach might enhance user comfort as well. The BIFB method is tested on two online benchmark datasets and outperforms the compared methods. The results show the potential of bio-inspired design, and the findings will be extended by including further SSVEP characteristics for future SSVEP based BCIs.
Highlights
Scientific advances in neuroscience and biomedical engineering enabled a direct communication channel between the human brain and a computer
The proposed bio-inspired filter banks (BIFBs) method for state visual evoked potential (SSVEP) frequency recognition is tested on two datasets that include EEG recordings of eight subjects in 152 trials
The BIFBs are promising in the highfrequency band where signal to noise ratio (SNR) is low. This method increases the information transfer rate (ITR) of an SSVEP based Brain-computer interfaces (BCIs) and might improve its user comfort due to less visual fatigue
Summary
Scientific advances in neuroscience and biomedical engineering enabled a direct communication channel between the human brain and a computer. A brain-computer interface (BCI) [2] analyzes the brain signals and translates them into commands for external devices such as a speller device, wheelchair, robotic arm, or a drone (Fig. 1). Advanced BCI systems serve healthy people as well by providing an alternative way of communication, control, and security [3]–[5]. These systems have evolved to be a promising part of the body area network [6]–[10]
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