Steady-state Visual Evoked Potential Research Articles

Comparison of Deep Learning and Traditional Machine Learning Classification Performance in a SSVEP Based Brain Computer Interface

Brain-computer interfaces (BCIs) offer a very high potential to help those who cannot use their organs properly. In the literature, many electroencephalogram based BCIs exist. Steady state visual evoked potential (SSVEP) based BCIs provide relatively higher accuracy values which make them very popular in BCI research. Recently, deep learning (DL) based methods have been used in electroencephalogram classification problems and they had superior performance over traditional machine learning (ML) methods, which require feature extraction step. This study aimed at comparing the performance of DL and traditional ML based classification performance in terms of stimuli duration, number of channels, and number of trials in an SSVEP based BCI experiment. In the traditional approach canonical correlation analysis method was used for the feature extraction and then three well-known classifiers were used for classification. In DL-based classification, spatio-spectral decomposition (SSD) method was integrated as a preprocessing step to extract oscillatory signals in the frequency band of interest with a convolutional neural network structure. Obtained offline classification results show that proposed DL approach could generate better accuracy values than traditional ML-based methods for short time segments (< 1 s). Besides, use of SSD as a preprocessing step increased the accuracy of DL classification. Superior performance of proposed SSD based DL approach over the traditional ML methods in short trials shows the feasibility of this approach in future BCI designs. Similar approach can be used in other fields where there are oscillatory activity in the recorded signals.

Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis.

Background: The brain–computer interface (BCI) is a highly cross-discipline technology and its successful application in various domains has received increasing attention. However, the BCI-enabled automobile industry is has been comparatively less investigated. In particular, there are currently no studies focusing on brain-controlled driving mode selection. Specifically, different driving modes indicate different driving styles which can be selected according to the road condition or the preference of individual drivers. Methods: In this paper, a steady-state visual-evoked potential (SSVEP)-based driving mode selection system is proposed. Upon this system, drivers can select the intended driving modes by only gazing at the corresponding SSVEP stimuli. A novel EEG processing algorithm named inter-trial distance minimization analysis (ITDMA) is proposed to enhance SSVEP detection. Both offline and real-time experiments were carried out to validate the effectiveness of the proposed system. Conclusion: The results show that a high selection accuracy of up to 92.3% can be realized, although this depends on the specific choice of flickering duration, the number of EEG channels, and the number of training signals. Additionally, energy consumption is investigated in terms of which the proposed brain-controlled system considerably differs from a traditional driving mode selection system, and the main reason is shown to be the existence of a detection error.

Comparing the Usability of Alternative EEG Devices to Traditional Electrode Caps for SSVEP-BCI Controlled Assistive Robots.

Despite having the potential to improve the lives of severely paralyzed users, non-invasive Brain Computer Interfaces (BCI) have yet to be integrated into their daily lives. The widespread adoption of BCI-driven assistive technology is hindered by its lacking usability, as both end-users and researchers alike find fault with traditional EEG caps. In this paper, we compare the usability of four EEG recording devices for Steady-State Visually Evoked Potentials (SSVEP)-BCI applications: an EEG cap (active gel electrodes), two headbands (passive gel or active dry electrodes), and two adhesive electrodes placed on each mastoid. Ten able-bodied participants tested each device by completing an 8-target SSVEP paradigm. Setup times were recorded, and participants rated their satisfaction with each device. The EEG cap obtained the best classification accuracies (Median = 98.96%), followed by the gel electrode headband (Median = 93.75%), and the dry electrode headband (Median = 91.14%). The mastoid electrodes obtained classification accuracies close to chance level (Med = 29.69%). Unknowing of the classification accuracy, participants found the mastoid electrodes to be the most comfortable and discrete. The dry electrode headband obtained the lowest user satisfaction score and was criticized for being too uncomfortable. Participants also noted that the EEG cap was too conspicuous. The gel-based headband provided a good trade-off between BCI performance and user satisfaction.

Steady-state visual evoked potentials in children with neurofibromatosis type 1: associations with behavioral rating scales and impact of psychostimulant medication

BackgroundNeurofibromatosis type 1 (NF1) is a genetic disorder often associated with cognitive dysfunctions, including a high occurrence of deficits in visuoperceptual skills. The neural underpinnings of these visuoperceptual deficits are not fully understood. We used steady-state visual evoked potentials (SSVEPs) to investigate possible alterations in the synchronization of neural activity in the occipital cortex of children with NF1.MethodsSSVEPs were measured using electroencephalography and compared between children with NF1 (n = 28) and neurotypical controls (n = 28) aged between 4 and 13 years old. SSVEPs were recorded during visual stimulation with coloured icons flickering at three different frequencies (6 Hz, 10 Hz, and 15 Hz) and analyzed in terms of signal-to-noise ratios. A mixed design ANCOVA was performed to compare SSVEP responses between groups at the three stimulation frequencies. Pearson’s correlations with levels of intellectual functioning as well as with symptoms of ADHD, ASD and emotional/behavioral problems were performed. The impact of psychostimulant medication on the SSVEP responses was analyzed in a subset of the NF1 group (n = 8) with paired t-tests.ResultsWe observed reduced signal-to-noise ratios of the SSVEP responses in children with NF1. The SSVEP responses were negatively correlated with symptoms of inattention and with symptoms of emotional/behavioral problems in the NF1 group. The SSVEP response generated by the lowest stimulation frequency (i.e., 6 Hz) was rescued with the intake of psychostimulant medication.ConclusionsImpaired processing of rhythmic visual stimulation was evidenced in children with NF1 through measures of SSVEP responses. Those responses seem to be more reduced in children with NF1 who exhibit more symptoms of inattention and emotional/behavioral problems in their daily life. SSVEPs are potentially sensitive electrophysiological markers that could be included in future studies investigating the impact of medication on brain activity and cognitive functioning in children with NF1.

Using Neurofeedback from Steady-State Visual Evoked Potentials to Target Affect-Biased Attention in Augmented Reality.

Biases in attention to emotional stimuli (i.e., affect-biased attention) contribute to the development and mainte-nance of depression and anxiety and may be a promising target for intervention. Past attempts to therapeutically modify affect-biased attention have been unsatisfactory due to issues with reliability and precision. Electroencephalogram (EEG)-derived steady-state visual evoked potentials (SSVEPS) provide a temporally-sensitive biological index of attention to competing visual stimuli at the level of neuronal populations in the visual cortex. SSVEPS can potentially be used to quantify whether affective distractors vs. task-relevant stimuli have "won" the competition for attention at a trial-by-trial level during neuro-feedback sessions. This study piloted a protocol for a SSVEP-based neurofeedback training to modify affect-biased attention using a portable augmented-reality (AR) EEG interface. During neurofeedback sessions with five healthy participants, signifi-cantly greater attention was given to the task-relevant stimulus (a Gabor patch) than to affective distractors (negative emotional expressions) across SSVEP indices (p<0.000l). SSVEP indices exhibited excellent internal consistency as evidenced by a maximum Guttman split-half coefficient of 0.97 when comparing even to odd trials. Further testing is required, but findings suggest several SSVEP neurofeedback calculation methods most deserving of additional investigation and support ongoing efforts to develop and implement a SSVEP-guided AR-based neurofeedback training to modify affect-biased attention in adolescent girls at high risk for depression.

Brain-Computer Interfaces: Investigating the Transition from Visually Evoked to Purely Imagined Steady-State Potentials.

Brain-Computer Interfaces (BCIs) based on Steady State Visually Evoked Potentials (SSVEPs) have proven effective and provide significant accuracy and information-transfer rates. This family of strategies, however, requires external devices that provide the frequency stimuli required by the technique. This limits the scenarios in which they can be applied, especially when compared to other BCI approaches. In this work, we have investigated the possibility of obtaining frequency responses in the EEG output based on the pure visual imagination of SSVEP-eliciting stimuli. Our results show that not only that EEG signals present frequency-specific peaks related to the frequency the user is focusing on, but also that promising classification accuracy can be achieved, paving the way for a robust and reliable visual imagery BCI modality. Clinical relevance-Brain computer interfaces play a fundamental role in enhancing the quality of life of patients with severe motor impairments. Strategies based on purely imagined stimuli, like the one presented here, are particularly impacting, especially in the most severe cases.

Development of an Ultra Low-Cost SSVEP-based BCI Device for Real-Time On-Device Decoding.

This study details the development of a novel, approx. £20 electroencephalogram (EEG)-based brain-computer interface (BCI) intended to offer a financially and operationally accessible device that can be deployed on a mass scale to facilitate education and public engagement in the domain of EEG sensing and neurotechnologies. Real-time decoding of steady-state visual evoked potentials (SSVEPs) is achieved using variations of the widely-used canonical correlation analysis (CCA) algorithm: multi-set CCA and generalised CCA. All BCI functionality is executed on board an inexpensive ESP32 microcontroller. SSVEP decoding accuracy of 95.56 ± 3.74% with an ITR of 102 bits/min was achieved with modest calibration.

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm.

Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Stäubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Stäubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system.

뇌-컴퓨터 인터랙션 오탐 저감을 위한 뇌파-턱 근전도 통합 신호 제어 프로토콜 개발

Objective: The purpose of this study was to investigate whether electromyogram (EMG) induced by jaw-clenching can be accurately observed in steady-state visually evoked potential (SSVEP) signals at occipital lobes as well as temporal lobes. We also investigated a feasibility that the fused SSVEP data with the jaw-clenching EMG can be classified as a function of task types characterized by SSVEPs with and without the short and long clenching noise. Based on this hypothesis, we proposed a novel command protocol to effectively and efficiently reduce false alarms caused by using SSVEP brain-computer interface (BCI).<BR><BR>Background: Much attention has been paid to various brain-computer interfaces for neural rehabilitation and alternative communication channels for disabled people with severe neurological disorders. However, despite the interests in BCI, false alarms caused by interference effects between command flickers with different frequencies or internal and external noises have been still problematic and have yet to be investigated. Even though protocols to correct the false alarms based on error-related potential (ErrP) or eye blinks have been proposed, it has still limitation due to errors inherent in involuntary reactions of humans. Thus, it is necessary to develop a protocol to address the issue and propose a method insusceptible to the involuntary reactions.<BR><BR>Method: Nine undergraduate students (3 female) voluntarily participated in the experiment. They were asked to divide their attention between the two squares randomly oscillating with 8.57 and 10Hz and perform three types of tasks characterized by SSVEP only, short clenching, and long clenching conditions. They carried out the three tasks with a semi-counterbalanced and random order and rest interval between the tasks was set to 10 minutes to minimize order and carryover effects. It took about 12 minutes in completing the experimental tasks for each participant. We used five machine learning algorithms of binary classification to compare the performance in classifying the brainwave signals characterized by the three tasks.<BR><BR>Results: As a result of the classification comparison, random forest showed the highest classification performance of more than 0.85 (AUC) as follows: 0.88 (short clenching vs. SSVEP), 0.87 (long clenching vs. SSVEP), 0.85 (long clenching vs. short clenching condition) in the test data set. Notably, the findings implicated a clear classification in the long cand short clenching condition even at the frequency band of more than 20Hz. For the participants whose highest weight factor was shown at the occipital lobe, it can be possible to reduce false alarms in the SSVEP BCI with only single electrode at the occipital lobe.<BR><BR>Conclusion: The findings found in this study implicate that the proposed protocol can be used to effectively and efficiently correct false alarms in event-related potential (ERP), event-related desynchronization/synchronization (ERD/ERS) BCI as well as SSVEP BCI. On top of that, classifying the subtle changes in the jaw clenching types enables BCI developers to create additional commands to modulate their BCI system. The results can be greatly improved if weight factors in the binary classification are modulated as a function of individual characteristics.<BR><BR>Application: The findings obtained in this study can be utilized in developing asynchronous BCI very robust to false alarms and expected to provide a future direction into hybrid BCI system.

Brain responses to lexical attestedness and phonological well-formedness as revealed by fast periodic visual stimulation

We investigated the mechanisms underlying the online-processing of phonological constraints using oddball fast-periodic visual stimulation coupled with EEG. We focused on the Sonority Sequencing Principle and examined whether steady-state visual evoked potentials (SSVEPs) are sensitive to the sonority constraint on syllable onsets. Native French speakers were presented with streams of CCVC non-words (C: consonant, V: vowel) at a fixed 6-Hz base rate. We manipulated the phonological well-formedness and lexical attestedness of CC onsets in two conditions. SSVPs were observed at the base rate associated to visual stimuli. As expected, they did not differ between conditions. Oddball SSVEPs were observed at 1.2 Hz (and its harmonics) and differed in the two conditions. These results showed that SSVEPs are sensitive to sublexical features. They also suggest that the processing of phonological constraints rely on mechanisms which could be dissociated from those underlying the processing of statistical properties of the lexicon.

Step VEP visual acuity in a pediatric neuro-ophthalmological cohort

Steady-state VEPs, have been used to estimate visual acuity since the 1970s and allow responses to a range of stimulus sizes to be collected rapidly- with particular utility in infants. However, the assessment of children with cortical visual impairment is a bigger challenge that lead to the development of the Step VEP. Its initial evaluation revealed that accuracy and precision were poorer for pediatric patients than for optically degraded normal adults and that it was not necessarily successful in every child. Statistical models generated the equations: VAO = 0.56 VAStep (r2 = 0.75, F = 60.93, p = 0.000) and VAPL = 0.45 VAStep (r2 = 0.82, F = 156.85, p = 0.000), supported by a recent a systematic review of VA comparisons showing that recognition VA (optotypes) agrees more closely than discrimination VA (PL) with VEP VA. In combination, Step VEPS and subjective tests allowed complete assessment in 96% of patients, with incomplete Step VEPS much more likely to be partially successful than not, and more likely to be partially successful than incomplete subjective tests. This supports the rationale that Step VEPs maintain attention by limiting the time spent stimulating away from an individual’s threshold of spatial resolution. For the small number of patients in whom VA cannot be estimated, alternative stimuli and methods of presentation are proposed.

DSCNN: Dilated Shuffle CNN Model for SSVEP Signal Classification

The steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) system has attracted a lot of attention. It is a great challenge to increase the classification accuracy of SSVEP, especially in fatigue state. In this paper, we propose a dilated shuffle convolutional neural network (DSCNN) model to realize EEG-based SSVEP signal classification. Firstly, we conduct experiments to obtain SSVEP recordings in normal and fatigue states. Then combining continuous wavelet transform (CWT) and DSCNN, we construct a framework for realizing the SSVEP detection. In DSCNN, the signals are processed by three parallel dilated convolution layers firstly, then we extract the characteristics of the signals through channel shuffle and group convolution, while reducing the computational load. For normal condition, we reach an average accuracy rate of 96.75%, and for the data under fatigue state, the average accuracy of this method increases to 77.52%. Through the comparison with the existing methods, the effectiveness and advance of our method are proved, and the effect of channel shuffle on signal extraction is also demonstrated by comparison.

Optimising the classification of feature-based attention in frequency-tagged electroencephalography data

Brain-computer interfaces (BCIs) are a rapidly expanding field of study and require accurate and reliable real-time decoding of patterns of neural activity. These protocols often exploit selective attention, a neural mechanism that prioritises the sensory processing of task-relevant stimulus features (feature-based attention) or task-relevant spatial locations (spatial attention). Within the visual modality, attentional modulation of neural responses to different inputs is well indexed by steady-state visual evoked potentials (SSVEPs). These signals are reliably present in single-trial electroencephalography (EEG) data, are largely resilient to common EEG artifacts, and allow separation of neural responses to numerous concurrently presented visual stimuli. To date, efforts to use single-trial SSVEPs to classify visual attention for BCI control have largely focused on spatial attention rather than feature-based attention. Here, we present a dataset that allows for the development and benchmarking of algorithms to classify feature-based attention using single-trial EEG data. The dataset includes EEG and behavioural responses from 30 healthy human participants who performed a feature-based motion discrimination task on frequency tagged visual stimuli.

SSVEP-based brain–computer interface for music using a low-density EEG system

ABSTRACT In this paper, we present a bespoke brain–computer interface (BCI), which was developed for a person with severe motor-impairments, who was previously a Violinist, to allow performing and composing music at home. It uses steady-state visually evoked potential (SSVEP) and adopts a dry, low-density, and wireless electroencephalogram (EEG) headset. In this study, we investigated two parameters: (1) placement of the EEG headset and (2) inter-stimulus distance and found that the former significantly improved the information transfer rate (ITR). To analyze EEG, we adopted canonical correlation analysis (CCA) without weight-calibration. The BCI for musical performance realized a high ITR of 37.59 ± 9.86 bits min−1 and a mean accuracy of 88.89 ± 10.09%. The BCI for musical composition obtained an ITR of 14.91 ± 2.87 bits min−1 and a mean accuracy of 95.83 ± 6.97%. The BCI was successfully deployed to the person with severe motor-impairments. She regularly uses it for musical composition at home, demonstrating how BCIs can be translated from laboratories to real-world scenarios.

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Recently, steady-state visual evoked potential (SSVEP) has become one of the most popular electroencephalography paradigms due to its high information transfer rate. Several approaches have been proposed to improve the performance of SSVEP. The calibration-free scenario is significant in SSVEP-based brain–computer interface systems, where the subject is the first time to use the system. The participating teams proposed several effective calibration-free algorithm frameworks in the SSVEP competition (calibration-free) of the BCI Controlled Robot Contest in World Robot Contest 2021. This paper introduces the approaches used in the algorithms of the top five teams in the final. The results of the five subjects in the final proved the effectiveness of the approaches. This paper discusses the effectiveness of each approach in improving the system performance in the calibration-free scenario and gives suggestions on how to use these approaches in a real-world system.

Online Adaptation Boosts SSVEP-Based BCI Performance.

A user-friendly steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) prefers no calibration for its target recognition algorithm, however, the existing calibration-free schemes perform still far behind their calibration-based counterparts. To tackle this issue, learning online from the subject's unlabeled data is investigated as a potential approach to boost the performance of the calibration-free SSVEP-based BCIs. An online adaptation scheme is developed to tune the spatial filters using the online unlabeled data from previous trials, and then developing the online adaptive canonical correlation analysis (OACCA) method. A simulation study on two public SSVEP datasets (Dataset I and II) with a total of 105 subjects demonstrated that the proposed online adaptation scheme can boost the CCA's averaged information transfer rate (ITR) from 94.60 to 158.87 bits/min in Dataset I and from 85.80 to 123.91 bits/min in Dataset II. Furthermore, in our online experiment it boosted the CCA's ITR from 55.81 bits/min to 95.73 bits/min. More importantly, this online adaptation scheme can be easily combined with any spatial filtering-based algorithms to achieve online learning. By online adaptation, the proposed OACCA performed much better than the calibration-free CCA, and comparable to the calibration-based algorithms. This work provides a general way for the SSVEP-based BCIs to learn online from unlabeled data and thus avoid calibration.

The effect of stimulus number on the recognition accuracy and information transfer rate of SSVEP–BCI in augmented reality

Objective. The biggest advantage of steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) lies in its large command set and high information transfer rate (ITR). Almost all current SSVEP–BCIs use a computer screen (CS) to present flickering visual stimuli, which limits its flexible use in actual scenes. Augmented reality (AR) technology provides the ability to superimpose visual stimuli on the real world, and it considerably expands the application scenarios of SSVEP–BCI. However, whether the advantages of SSVEP–BCI can be maintained when moving the visual stimuli to AR glasses is not known. This study investigated the effects of the stimulus number for SSVEP–BCI in an AR context. Approach. We designed SSVEP flickering stimulation interfaces with four different numbers of stimulus targets and put them in AR glasses and a CS to display. Three common recognition algorithms were used to analyze the influence of the stimulus number and stimulation time on the recognition accuracy and ITR of AR–SSVEP and CS–SSVEP. Main results. The amplitude spectrum and signal-to-noise ratio of AR–SSVEP were not significantly different from CS–SSVEP at the fundamental frequency but were significantly lower than CS–SSVEP at the second harmonic. SSVEP recognition accuracy decreased as the stimulus number increased in AR–SSVEP but not in CS–SSVEP. When the stimulus number increased, the maximum ITR of CS–SSVEP also increased, but not for AR–SSVEP. When the stimulus number was 25, the maximum ITR (142.05 bits min−1) was reached at 400 ms. The importance of stimulation time in SSVEP was confirmed. When the stimulation time became longer, the recognition accuracy of both AR–SSVEP and CS–SSVEP increased. The peak value was reached at 3 s. The ITR increased first and then slowly decreased after reaching the peak value. Significance. Our study indicates that the conclusions based on CS–SSVEP cannot be simply applied to AR–SSVEP, and it is not advisable to set too many stimulus targets in the AR display device.

Novel Signal-to-Signal translation method based on StarGAN to generate artificial EEG for SSVEP-based brain-computer interfaces

Generative adversarial networks (GANs) have shown promising performance in image-to-image translation. Inspired by StarGAN v2, which was introduced to address the multidomain image-to-image translation, the authors propose a novel multidomain signal-to-signal translation method to generate artificial steady-state visual evoked potential (SSVEP) signals from resting electroencephalograms (EEGs). The proposed StarGAN-based signal-to-signal translation model was trained using EEG data acquired from three subjects and could successfully generate sufficient numbers of artificial SSVEP signals of 15 test participants using resting EEG data acquired over an extremely short time period (∼16 s) from each test participant. The possibility of improving the performance of SSVEP-based brain–computer interfaces (BCIs) was investigated by incorporating the artificially generated SSVEP signals and Combined-ECCA, which is proposed in this study as an extended version of combined canonical correlation analysis (Combined-CCA). Fifteen healthy individuals participated in the SSVEP-based BCI study to distinguish four visual stimuli with different flickering frequencies. To evaluate the degree of performance improvement, the average classification accuracy and information transfer rate (ITR) obtained using the proposed methods were compared with those obtained using filter bank CCA (FBCCA), a state-of-the-art training-free method for SSVEP-based BCIs. The performances in terms of the classification accuracy and ITR of SSVEP-based BCIs were significantly improved by using the proposed methods (95.47% and 40.09 bit/min) compared with those using FBCCA (92.03% and 31.80 bit/min, p < 0.05). In addition, more than four individual training trials per class were necessary to achieve better classification accuracy than that of the proposed approach if the conventional individual training approach was employed.

Cross-subject spatial filter transfer method for SSVEP-EEG feature recognition

Objective. Steady-state visual evoked potential (SSVEP) is an important control method of the brain–computer interface (BCI) system. The development of an efficient SSVEP feature decoding algorithm is the core issue in SSVEP-BCI. It has been proposed to use user training data to reduce the spontaneous electroencephalogram activity interference on SSVEP response, thereby improving the feature recognition accuracy of the SSVEP signal. Nevertheless, the tedious data collection process increases the mental fatigue of the user and severely affects the applicability of the BCI system. Approach. A cross-subject spatial filter transfer (CSSFT) method that transfer the existing user model with good SSVEP response to the new user test data without collecting any training data from the new user is proposed. Main results. Experimental results demonstrate that the transfer model increases the distinction of the feature discriminant coefficient between the gaze following target and the non-gaze following target and accurately identifies the wrong target in the fundamental algorithm model. The public datasets show that the CSSFT method significantly increases the recognition performance of canonical correlation analysis (CCA) and filter bank CCA. Additionally, when the data used to calculate the transfer model contains one data block only, the CSSFT method retains its effective feature recognition capabilities. Significance. The proposed method requires no tedious data calibration process for new users, provides an effective technical solution for the transfer of the cross-subject model, and has potential application value for promoting the application of the BCI system.

Towards a More Theory-Driven BCI Using Source Reconstructed Dynamics of EEG Time-Series

Currently, the operational electroencephalography (EEG)-based brain–computer interfaces (BCIs) suffer from problems of BCI latency/lag issues, which restricts the use of interfaces impractical scenarios. One of the reasons behind the present challenges is the application of a purely data-driven approach to the BCI pipeline. Although BCI applications have improved significantly with the research in the fields of artificial intelligence (AI) and machine learning (ML), fundamental issues of data-driven training restrict the latency that can be achieved under current BCI paradigms. This work explores the possibility of future BCI using a combination of data-driven and theory-driven methods. In this study, an EEG-BCI dataset from steady-state visually evoked potentials (SSVEPs) is applied, where the SSVEP signals contain, source components from the occipital, parietal and frontal regions of the brain. Source reconstruction is done with the combination of independent component analysis (ICA) and low-resolution electromagnetic tomography analysis (LORETA). This method was able to predict BCI classification labels 5[Formula: see text]s earlier, based on pre-recorded signals from the scalp. The novelty of the current contribution lies in utilizing the source reconstructed EEG time-series for BCI classification, which allows for retention of classification accuracy up to 70% while working with the reduced data dimensionality. Implementation of this algorithm will allow a significant reduction in lag in online BCIs.