SSVEP-assisted RSVP brain–computer interface paradigm for multi-target classification

Abstract

Brain–computer Interface (BCI) is actively involved in optimizing the communication medium between the human brain and external devices.Objective. Rapid serial visual presentation (RSVP) is a robust and highly efficient BCI technique in recognizing target objects but suffers from limited target selections. Hybrid BCI systems that combine steady-state visual evoked potential (SSVEP) and RSVP can mitigate this limitation and allow users to operate on multiple targets. Approach. This study proposes a novel hybrid SSVEP-RSVP BCI to improve the performance of classifying the target/non-target objects in a multi-target scenario. In this paradigm, SSVEP stimulation helps in identifying the user’s focus location and RSVP stimuli that elicit event-related potentials differentiate target and non-target objects. Main results. The proposed model achieved an offline accuracy of 81.59% by using 12 electroencephalography (EEG) channels and an online (real-time) accuracy of 78.10% when only four EEG channels are considered. Further, the biomarkers of physiological states are analyzed to assess the cognitive states (mental fatigue and user attention) of the participants based on resting theta and alpha band powers. The results indicate an inverse relationship between the BCI performance and the resting EEG power, validating that the subjects’ performance is affected by physiological states for long-term use of the BCI. Significance. Our findings demonstrate that the combination of SSVEP and RSVP stimuli improves the BCI performance and further enhances the possibility of performing multiple user command tasks, which are inevitable in real-world applications. Additionally, the cognitive state biomarkers discussed imply the need for an efficient and attractive experimental paradigm that reduces the physiological state disparities and provide enhanced BCI performance.

Studies to Overcome Brain–Computer Interface Challenges

“…Over the past decades, there have been lots of BCI studies. They were usually focused on the methods to improve the prediction accuracy [5,8,23,[30][31][32], raise the number of commands [12,33], increase the information transfer rate (ITR) [34][35][36][37][38], or reduce the training efforts [7,30,34,39]. To enhance the prediction accuracy, new classification algorithms [30,40,41] or feature extraction methods have been proposed [31,32,42].…”

EEG hybrid brain-computer interfaces: A scoping review applying an existing hybrid-BCI taxonomy and considerations for pediatric applications

“…No studies included child participants. One study had a participant aged 18 years (Kaongoen and Jo, 2017) but most studies had at least one participant within the range of 20-30 years, except for Nann et al (2020) who worked with tetraplegic participants aged 51.8 ± 15.2 years. Only two papers included participants above 40 years of age (Brennan et al, 2020; Nann 2016) were omitted from the graph because their average accuracies were less than 70% (44.5 and 60%, respectively).…”

“…There have been several reviews on hBCI but they primarily included studies that tested systems with adults who do not have disabilities: Sharmila (2020) provided an overview on the types of hBCI for wheelchair-based systems; Neeling and Hulle (2019) focused on multi-input hybrids and their applications; Sadeghi and Maleki (2018) compared accuracy and information transfer rate (ITR) across systems; Hong and Khan (2017) discussed the combination of brain signals and their application for both clinical and non-clinical scenarios; Choi et al (2017) did a systematic review and proposed a taxonomy classification system for hBCI systems; Banville and Falk (2016) did a systematic review and discussed experimental protocols, signal processing, and study rational; and Amiri et al (2013) reviewed multi-brain signal hBCIs. Muller-Putz et al (2015) compared hBCI applications that had participants with and without disabilities.…”

A Deep Learning Approach for Brain Computer Interaction-Motor Execution EEG Signal Classification

“…On the other hand, tables 5 and 6 give more enhanced results compared with [57] developed model when we applied the DBN algorithm. Ko et al [58] developed a hybrid SSVEP-RSVP BCI model to improve the performance of classifying the target/non-target objects in a multi-target scenario by using 12-EEG channels. In addition, by applying the decision tree classifier, the experimental results showed that our model performs better than [58] that used the Bagging Tree algorithm classifier with a mean accuracy of 83.45%, as shown in tables 5 and 6.…”