Steady-state Visual Evoked Potential Research Articles

Efficient dual-frequency SSVEP brain-computer interface system exploiting interocular visual resource disparities

Here, we present a novel dual-frequency steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) system with a unique human–computer interaction (HCI) feature that utilizes distinct visual resource disparities between the two eyes. By leveraging subtle differences in electroencephalography (EEG) responses due to binocular disparity, our system effectively distinguishes identical frequency targets. We conducted a two-phase validation: initial offline experiments confirmed EEG differences from interocular disparity, followed by global optimization to refine the SSVEP coding and spatial arrangement, allowing for 40 distinct targets within 20 frequency combinationes. A tailored binocular-task-related component analysis (bTRCA) algorithm was developed, achieving a maximum individual information transmission rate (ITR) of 338.32 bits/min offline and an average of 224.96 bits/min, with the online version reaching an average ITR of 234.13 bits/min. These results indicate that this system is a leading candidate for dual-frequency SSVEP BCIs, opening new prospects for HCI applications.

Discriminating brainwave patterns of different control and non-control states for enhancing asynchronous brain-computer interfaces

ObjectiveIn asynchronous brain-computer interfaces (BCIs) for real-world applications, the user typically remains in non-control states most of the time and is allowed to perform various types of non-control tasks. Because of the diversity of non-control states, which include stimuli-off-based non-control (SNC), rest-based non-control (RNC), feedback-based non-control (FNC), and game-based non-control (GNC), it is difficult to discriminate between them. The objective of this study was to develop a discrimination method for different control and non-control states to enhance brain–robot cooperation. MethodsAn individualized space-frequency-based optimized complex network (ISF-OCN) was designed to discriminate the electroencephalogram (EEG) patterns of five typical states: a steady-state visual evoked potential (SSVEP)-based control (SC) state and four non-control states, i.e., SNC, RNC, FNC, and GNC. The ISF-OCN individualizes frequency bands and spatial filters to enhance the optimized complex network features for each state and uses random forest classifiers in a one-versus-rest scheme for classification.Main results: The proposed ISF-OCN outperformed four existing algorithms in both binary classification and five-state classification, regardless of data length. Notably, the ISF-OCN algorithm with a data length of 1.5 s achieved an average accuracy of 84.51 % in recognizing the five different states. ConclusionThe results demonstrate the feasibility of discriminating the four non-control states using EEG features and suggest that the proposed method is a promising new approach for discriminating control and non-control states to enhance asynchronous BCIs.

Frequency-dependent dynamics of steady-state visual evoked potentials under sustained flicker stimulation

Steady-state visual evoked potentials (SSVEP) are electroencephalographic signals elicited when the brain is exposed to a visual stimulus with a steady frequency. We analyzed the temporal dynamics of SSVEP during sustained flicker stimulation at 5, 10, 15, 20 and 40 Hz. We found that the amplitudes of the responses were not stable over time. For a 5 Hz stimulus, the responses progressively increased, while, for higher flicker frequencies, the amplitude increased during the first few seconds and often showed a continuous decline afterward. We hypothesize that these two distinct sets of frequency-dependent SSVEP signal properties reflect the contribution of parvocellular and magnocellular visual pathways generating sustained and transient responses, respectively. These results may have important applications for SSVEP signals used in research and brain-computer interface technology and may contribute to a better understanding of the frequency-dependent temporal mechanisms involved in the processing of prolonged periodic visual stimuli.

WalkingWizard—A Truly Wearable EEG Headset for Everyday Use

Electroencephalography (EEG) provides an opportunity to gain insights to electrocortical activity without the need for invasive technology. While increasingly used in various application areas, EEG headsets tend to be suited only to a laboratory environment due to the long preparation time to don the headset and the need for users to remain stationary. We present our design of a dry, dual-electrodes flexible PCB assembly that realizes accurate sensing in the face of practical motion artifacts. Using it, we present WalkingWizard, our prototype dry-electrode EEG baseball cap that can be used under motion in everyday scenarios. We first evaluated its hardware performance by comparing its electrode-scalp impedance and ability to capture alpha rhythm against both wet EEG and commercially available dry EEG headsets. We then tested WalkingWizard using steady-state visual evoked potential (SSVEP) experiments, achieving high classification accuracy of 87% for walking speeds up to 5.0 km/h, beating state-of-the-art. Expanding on WalkingWizard, we integrated all necessary electronic components into a flexible PCB assembly—realizing WalkingWizard Integrated , in a truly wearable form-factor. Utilizing WalkingWizard Integrated, we demonstrated several applications as proof-of-concept: classification of SSVEP in VR environment while walking, real-time acquisition of emotional state of users while moving around the neighbourhood, and understanding the effect of guided meditation for relaxation.

A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network.

For the brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP), it is difficult to obtain satisfactory classification performance for short-time window SSVEP signals by traditional methods. In this paper, a fused multi-subfrequency bands and convolutional block attention module (CBAM) classification method based on convolutional neural network (CBAM-CNN) is proposed for discerning SSVEP-BCI tasks. This method extracts multi-subfrequency bands SSVEP signals as the initial input of the network model, and then carries out feature fusion on all feature inputs. In addition, CBAM is embedded in both parts of the initial input and feature fusion for adaptive feature refinement. To verify the effectiveness of the proposed method, this study uses the datasets of Inner Mongolia University of Technology (IMUT) and Tsinghua University (THU) to evaluate the performance of the proposed method. The experimental results show that the highest accuracy of CBAM-CNN reaches 0.9813 percentage point (pp). Within 0.1-2s time window, the accuracy of CBAM-CNN is 0.0201-0.5388 (pp) higher than that of CNN, CCA-CWT-SVM, CCA-SVM, CCA-GNB, FBCCA, and CCA. Especially in the short-time window range of 0.1-1s, the performance advantage of CBAM-CNN is more significant. The maximum information transmission rate (ITR) of CBAM-CNN is 503.87bit/min, which is 227.53bit/min-503.41bit/min higher than the above six EEG decoding methods. The study further results show that CBAM-CNN has potential application value in SSVEP decoding.

Visual cortical area contributions to the transient, multifocal and steady-state VEP: A forward model-informed analysis

Abstract Central to our understanding of how visual evoked potentials (VEPs) contribute to visual processing is the question of where their anatomical sources are. Three well-established measures of low-level visual cortical activity are widely used: the first component (“C1”) of the transient and multifocal VEP, and the steady-state VEP (SSVEP). Although primary visual cortex (V1) activity has often been implicated in the generation of all three signals, their dominant sources remain uncertain due to the limited resolution and methodological heterogeneity of source modelling. Here, we provide the first characterisation of all three signals in one analytic framework centred on the ‘cruciform model’, which describes how scalp topographies of V1 activity vary with stimulus location due to the retinotopy and unique folding pattern of V1. We measured the transient C1, multifocal C1, and SSVEPs driven by an 18.75Hz and 7.5Hz flicker, and regressed them against forward-models of areas V1, V2 and V3 generated from the Benson-2014 retinotopy atlas. The topographic variations of all four VEP signals across the visual field were better captured by V1 models, explaining between 2-6 times more variance than V2/V3. Models with all three visual areas improved fit further, but complementary analyses of temporal dynamics across all three signals indicated that the bulk of extrastriate contributions occur considerably later than V1. Overall, our data support the use of peak C1 amplitude and SSVEPs to probe V1 activity, although the SSVEP contains stronger extrastriate contributions. Moreover, we provide elaborated heuristics to distinguish visual areas in VEP data based on signal lateralization as well as polarity inversion.

Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain–computer interface and exoskeleton

Background:Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human–computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling. New method:Based on the neurofeedback training provided by the combination of brain–computer interface (BCI) and exoskeleton, this paper proposes a multimodal brain-controlled active rehabilitation system to help improve limb function. The joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) is adopted to achieve self-paced control and thus maximize the degree of brain involvement, and a requirement selection function based on SSVEP design is added to facilitate communication with aphasia patients. Comparison with existing methods:In addition, the Transformer is introduced as the MI decoder in the asynchronous online BCI to improve the global perception of electroencephalogram (EEG) signals and maintain the sensitivity and efficiency of the system. Results:In two multi-task online experiments for left hand, right hand, foot and idle states, subject achieves 91.25% and 92.50% best accuracy, respectively. Conclusion:Compared with previous studies, this paper aims to establish a high-performance and low-latency brain-controlled rehabilitation system, and provide an independent and autonomous control mode of the brain, so as to improve the effect of neural remodeling. The performance of the proposed method is evaluated through offline and online experiments.

A Modified Hybrid Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potentials and Electromyogram.

To enhance the information transfer rate (ITR) of a steady-state visual evoked potential (SSVEP)-based speller, more characters with flickering symbols should be used. Increasing the number of symbols might reduce the classification accuracy. A hybrid brain-computer interface (BCI) improves the overall performance of a BCI system by taking advantage of two or more control signals. In a simultaneous hybrid BCI, various modalities work with each other simultaneously, which enhances the ITR. In our proposed speller, simultaneous combination of electromyogram (EMG) and SSVEP was applied to increase the ITR. To achieve 36 characters, only nine stimulus symbols were used. Each symbol allowed the selection of four characters based on four states of muscle activity. The SSVEP detected which symbol the subject was focusing on and the EMG determined the target character out of the four characters dedicated to that symbol. The frequency rate for character encoding was applied in the EMG modality and latency was considered in the SSVEP modality. Online experiments were carried out on 10 healthy subjects. The average ITR of this hybrid system was 96.1 bit/min with an accuracy of 91.2%. The speller speed was 20.9 char/min. Different subjects had various latency values. We used an average latency of 0.2 s across all subjects. Evaluation of each modality showed that the SSVEP classification accuracy varied for different subjects, ranging from 80% to 100%, while the EMG classification accuracy was approximately 100% for all subjects. Our proposed hybrid BCI speller showed improved system speed compared with state-of-the-art systems based on SSVEP or SSVEP-EMG, and can provide a user-friendly, practical system for speller applications.

Prior probability cues bias sensory encoding with increasing task exposure

When observers have prior knowledge about the likely outcome of their perceptual decisions, they exhibit robust behavioural biases in reaction time and choice accuracy. Computational modelling typically attributes these effects to strategic adjustments in the criterion amount of evidence required to commit to a choice alternative - usually implemented by a starting point shift - but recent work suggests that expectations may also fundamentally bias the encoding of the sensory evidence itself. Here, we recorded neural activity with EEG while participants performed a contrast discrimination task with valid, invalid, or neutral probabilistic cues across multiple testing sessions. We measured sensory evidence encoding via contrast-dependent steady-state visual-evoked potentials (SSVEP), while a read-out of criterion adjustments was provided by effector-selective mu-beta band activity over motor cortex. In keeping with prior modelling and neural recording studies, cues evoked substantial biases in motor preparation consistent with criterion adjustments, but we additionally found that the cues produced a significant modulation of the SSVEP during evidence presentation. While motor preparation adjustments were observed in the earliest trials, the sensory-level effects only emerged with extended task exposure. Our results suggest that, in addition to strategic adjustments to the decision process, probabilistic information can also induce subtle biases in the encoding of the evidence itself.

Prior probability cues bias sensory encoding with increasing task exposure.

When observers have prior knowledge about the likely outcome of their perceptual decisions, they exhibit robust behavioural biases in reaction time and choice accuracy. Computational modelling typically attributes these effects to strategic adjustments in the criterion amount of evidence required to commit to a choice alternative - usually implemented by a starting point shift - but recent work suggests that expectations may also fundamentally bias the encoding of the sensory evidence itself. Here, we recorded neural activity with EEG while participants performed a contrast discrimination task with valid, invalid, or neutral probabilistic cues across multiple testing sessions. We measured sensory evidence encoding via contrast-dependent steady-state visual-evoked potentials (SSVEP), while a read-out of criterion adjustments was provided by effector-selective mu-beta band activity over motor cortex. In keeping with prior modelling and neural recording studies, cues evoked substantial biases in motor preparation consistent with criterion adjustments, but we additionally found that the cues produced a significant modulation of the SSVEP during evidence presentation. While motor preparation adjustments were observed in the earliest trials, the sensory-level effects only emerged with extended task exposure. Our results suggest that, in addition to strategic adjustments to the decision process, probabilistic information can also induce subtle biases in the encoding of the evidence itself.

A calibration-free c-VEP based BCI employing narrow-band random sequences

Objective. Code-modulated visual evoked potential (c-VEP) based brain–computer interfaces (BCIs) exhibit high encoding efficiency. Nevertheless, the majority of c-VEP based BCIs necessitate an initial training or calibration session, particularly when the number of targets expands, which impedes the practicality. To address this predicament, this study introduces a calibration-free c-VEP based BCI employing narrow-band random sequences. Approach. For the encoding method, a series of random sequences were generated within a specific frequency band. The c-VEP signals were subsequently elicited through the application of on-type grid flashes that were modulated by these sequences. For the calibration-free decoding algorithm, filter-bank canonical correlation analysis (FBCCA) was utilized with the reference templates generated from the original sequences. Thirty-five subjects participated into an online BCI experiment. The performances of c-VEP based BCIs utilizing narrow-band random sequences with frequency bands of 15–25 Hz (NBRS-15) and 8–16 Hz (NBRS-8) were compared with that of a steady-state visual evoked potential (SSVEP) based BCI within a frequency range of 8–15.8 Hz. Main results. The offline analysis results demonstrated a substantial correlation between the c-VEPs and the original narrow-band random sequences. After parameter optimization, the calibration-free system employing the NBRS-15 frequency band achieved an average information transfer rate (ITR) of 78.56 ± 37.03 bits/min, which exhibited no significant difference compared to the performance of the SSVEP based system when utilizing FBCCA. The proposed system achieved an average ITR of 102.1 ± 57.59 bits/min in a simulation of a 1000-target BCI system. Significance. This study introduces a novel calibration-free c-VEP based BCI system employing narrow-band random sequences and shows great potential of the proposed system in achieving a large number of targets and high ITR.

Multi-degree-of-freedom unmanned aerial vehicle control combining a hybrid brain-computer interface and visual obstacle avoidance

ObjectiveThe difficulty of unmanned aerial vehicle (UAV) control recently lies in multidirectional movement in 3-dimensional space, improving control accuracy and manipulation safety. To address these challenges, a UAV control system that incorporates a hybrid brain-computer interface (hBCI), gyroscope and visual obstacle avoidance based on monocular depth estimation is proposed. Approach. We propose an efficient steady-state visual evoked potential (SSVEP) classification network (CL-NET) featuring a one-dimensional convolutional neural network, a long short-term memory module and an attention module to identify the user's intention for UAV movement in the front, back, left and right directions. The take-off, landing and rising control of the UAV is realized by an electrooculogram (EOG) signal detection algorithm, a blink state detector. In addition, the UAV can fly in an oblique state and rotate according to the current head posture detected by a gyroscope. Furthermore, an improved monocular depth estimation network is employed to design the autonomous obstacle avoidance module of the UAV, ensuring the safety of the brain-controlled system in practice. Main results. The proposed CL-NET delivers an accuracy of 98.67% on the public dataset and an accuracy of 97.92% on the self-collected dataset, both of which surpass the performance of state-of-the-art models. Additionally, we set up a brain control group and a remote control group to conduct practical experiments in a realistic environment. In the experiments involving sixteen subjects, the proposed UAV control system reached an average information transfer rate (ITR) of 44.09 bits/min, and the brain control group had a lower collision rate than the remote control group. Significance. The hybrid control method ensures that the multi-degree-of-freedom (multi-DOF) UAV control system maintains outstanding performance while ensuring good safety.

The classification of SSVEP-BCI based on ear-EEG via RandOm Convolutional KErnel Transform with Morlet wavelet

During the advantages of shorter training and higher information throughput, steady-state visual evoked potential (SSVEP) is widely used in brain–computer interface (BCI) research. Recently, collecting EEG signals from the ear area (ear-EEG) has gained increasing attention because it is more comfortable and convenient than scalp electrodes. The ear-EEG-based BCI system based on ear electrodes has weaker signals and more noise components because the electrodes are located far away from the top of the head. In this study, the RandOm Convolutional KErnel Transform (ROCKET) algorithm integrated with the Morlet wavelet transform (Morlet-ROCKET) was proposed to solve this issue. This study compared the performence of Morlet-ROCKET with two established methods: canonical correlation analysis-based (FBCCA) and Transformer methods. The proposed Morlet-ROCKET model demonstrated superior performance across multiple measures, including increased classification accuracy in 1 s, 3 s, and 4 s time windows and higher area under the curve (AUC) values in receiver operating characteristic (ROC) analysis. The analysis result proved that with efficient data processing algorithms, ear-EEG-based BCI systems can also have good performance, and providing support for the popularization of BCI.

Steady-state Visual Evoked Potentials Reveal Dynamic (Re)allocation of Spatial Attention during Maintenance and Utilization of Visual Working Memory.

Visual working memory (VWM) allows to store goal-relevant information to guide future behavior. Prior work suggests that VWM is spatially organized and relies on spatial attention directed toward locations at which memory items were encoded, even if location is task irrelevant. Importantly, attention often needs to be dynamically redistributed between locations, for example, in preparation for an upcoming probe. Very little is known about how attentional resources are distributed between multiple locations during a VWM task and even less about the dynamic changes governing such attentional shifts over time. This is largely due to the inability to use behavioral outcomes to reveal fast dynamic changes within trials. We here demonstrate EEG steady-state visual evoked potentials (SSVEPs) to successfully track the dynamic allocation of spatial attention during a VWM task. Participants were presented with to-be-memorized gratings and distractors at two distinct locations, tagged with flickering discs. This allowed us to dynamically track attention allocated to memory and distractor items via their coupling with space by quantifying the amplitude and coherence of SSVEP responses in the EEG signal to flickering stimuli at the former memory and distractor locations. SSVEP responses did not differ between memory and distractor locations during early maintenance. However, shortly before probe comparison, we observed a decrease in SSVEP coherence over distractor locations indicative of a reallocation of spatial attentional resources. RTs were shorter when preceded by stronger decreases in SSVEP coherence at distractor locations, likely reflecting attentional shifts from the distractor to the probe or memory location. Broader Significance We demonstrate that SSVEPs can inform about dynamic processes in VWM, even if location does not have to be reported by participants. This finding not only supports the notion of a spatially organized VWM but also reveals that SSVEPs betray a dynamic prioritization process of working memory items and locations over time that is directly predictive of memory performance.

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding

Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user’s intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.

Realness of face images can be decoded from non-linear modulation of EEG responses

Artificially created human faces play an increasingly important role in our digital world. However, the so-called uncanny valley effect may cause people to perceive highly, yet not perfectly human-like faces as eerie, bringing challenges to the interaction with virtual agents. At the same time, the neurocognitive underpinnings of the uncanny valley effect remain elusive. Here, we utilized an electroencephalography (EEG) dataset of steady-state visual evoked potentials (SSVEP) in which participants were presented with human face images of different stylization levels ranging from simplistic cartoons to actual photographs. Assessing neuronal responses both in frequency and time domain, we found a non-linear relationship between SSVEP amplitudes and stylization level, that is, the most stylized cartoon images and the real photographs evoked stronger responses than images with medium stylization. Moreover, realness of even highly similar stylization levels could be decoded from the EEG data with task-related component analysis (TRCA). Importantly, we also account for confounding factors, such as the size of the stimulus face’s eyes, which previously have not been adequately addressed. Together, this study provides a basis for future research and neuronal benchmarking of real-time detection of face realness regarding three aspects: SSVEP-based neural markers, efficient classification methods, and low-level stimulus confounders.

The human subthalamic nucleus transiently inhibits active attentional processes.

The subthalamic nucleus (STN) of the basal ganglia is key to the inhibitory control of movement. Consequently, it is a primary target for the neurosurgical treatment of movement disorders like Parkinson's Disease, where modulating the STN via deep-brain stimulation (DBS) can release excess inhibition of thalamo-cortical motor circuits. However, the STN is also anatomically connected to other thalamo-cortical circuits, including those underlying cognitive processes like attention. Notably, STN-DBS can also affect these processes. This suggests that the STN may also contribute to the inhibition of non-motor activity, and that STN-DBS may cause changes to this inhibition. We here tested this hypothesis in humans. We used a novel, wireless outpatient method to record intracranial local field potentials (LFP) from STN DBS implants during a visual attention task (Experiment 1, N=12). These outpatient measurements allowed the simultaneous recording of high-density EEG, which we used to derive the steady-state visual evoked potential (SSVEP), a well-established neural index of visual attentional engagement. By relating STN activity to this neural marker of attention (instead of overt behavior), we avoided possible confounds resulting from STN's motor role. We aimed to test whether the STN contributes to the momentary inhibition of the SSVEP caused by unexpected, distracting sounds. Furthermore, we causally tested this association in a second experiment, where we modulated STN via DBS across two sessions of the task, spaced at least one week apart (N=21, no sample overlap with Experiment 1). The LFP recordings in Experiment 1 showed that reductions of the SSVEP after distracting sounds were preceded by sound-related γ-frequency (>60Hz) activity in the STN. Trial-to-trial modeling further showed that this STN activity statistically mediated the sounds' suppressive effect on the SSVEP. In Experiment 2, modulating STN activity via DBS significantly reduced these sound-related SSVEP reductions. This provides causal evidence for the role of the STN in the surprise-related inhibition of attention. These findings suggest that the human STN contributes to the inhibition of attention, a non-motor process. This supports a domain-general view of the inhibitory role of the STN. Furthermore, these findings also suggest a potential mechanism underlying some of the known cognitive side-effects of STN-DBS treatment, especially on attentional processes. Finally, our newly-established outpatient LFP recording technique facilitates the testing of the role of subcortical nuclei in complex cognitive tasks, alongside recordings from the rest of the brain, and in much shorter time than perisurgical recordings.

Effects of the presentation order of stimulations in sequential ERP/SSVEP Hybrid Brain-Computer Interface.

Hybrid Brain-Computer Interface (hBCI) combines multiple neurophysiology modalities or paradigms to speed up the output of a single command or produce multiple ones simultaneously. Concurrent hBCIs that employ endogenous and exogenous paradigms are limited by the reduced set of possible commands. Conversely, the fusion of different exogenous visual evoked potentials demonstrated impressive performances; however, they suffer from limited portability. Yet, sequential hBCIs did not receive much attention mainly due to slower transfer rate and user fatigueduring prolonged BCI use (Lorenz et al 2014 J. Neural Eng. 11 035007). Moreover, the crucial factors for optimizing the hybridization remain under-explored. In this paper, we test the feasibility of sequential Event Related-Potentials (ERP) and Steady-State Visual Evoked Potentials (SSVEP) hBCI and study the effect of stimulus order presentation between ERP-SSVEP and SSVEP-ERP for the control of directions and speed of powered wheelchairs or mobile robots with 15 commands. Exploiting the fast single trial face stimulus ERP, SSVEP and modern efficient convolutional neural networks, the configuration with SSVEP presented at first achieved significantly (p < 0.05) higher average accuracy rate with 76.39% ( ± 7.30 standard deviation) hybrid command accuracy and an average Information Transfer Rate (ITR) of 25.05 ( ± 5.32 standard deviation) bits per minute (bpm). The results of the study demonstrate the suitability of a sequential SSVEP-ERP hBCI with challenging dry electroencephalography (EEG) electrodes and low-compute capacity. Although it presents lower ITR than concurrent hBCIs, our system presents an alternative in small screen settings when the conditions for concurrent hBCIs are difficult to satisfy.

Impact of adaptive filtering-based component analysis method on steady-state visual evoked potential based brain computer interface systems

&lt;span lang="EN-US"&gt;The significance of brain computer interface (BCI) systems is immensely high, especially for disabled people and patients with nervous system failure. Therefore, in this study, adaptive filtering-based component analysis (AFCA) model is presented to enhance target box identification efficiency at varied flickering frequencies in a visual stimulation process by efficient acquisition of electroencephalogram (EEG) signals for the application of steady-state visually evoked potential based BCI system. Furthermore, optimization of proposed AFCA model is performed based on the maximized reproducibility of correlated components. A multimedia authoring and management using your eyes and mind (MAMEM) steady-state visual evoked potential (SSVEP) dataset is utilized for efficient training of EEG signals and background entities are eliminated using adaptive filters in a pre-processing stage. Additionally, spatial filtering components are obtained to detect target flickering box based on the obtained quality features. Performance is measured by acquisition of SSVEP signals in terms of reconstruction efficiency, classification accuracy and information transfer rate (ITR) using proposed AFCA model. Mean classification accuracy for all 11 subject is 93.48% and ITR is 308.23 bpm. Further, classification accuracy is relatively higher than various SSVEP classification algorithms.&lt;/span&gt;

Knocking out the LRRK2 gene increases sensitivity to wavelength information in rats

Leucine-rich repeat kinase 2 (LRRK2) is a gene related to familial Parkinson’s disease (PD). It has been associated with nonmotor symptoms such as disturbances in the visual system affecting colour discrimination and contrast sensitivity. This study examined how deficiency of LRRK2 impacts visual processing in adult rats. Additionally, we investigated whether these changes can be modelled in wild-type rats by administering the LRRK2 inhibitor PFE360. Visual evoked potentials (VEPs) and steady-state visual evoked potentials (SSVEPs) were recorded in the visual cortex and superior colliculus of female LRRK2-knockout and wild-type rats to study how the innate absence of LRRK2 changes visual processing. Exposing the animals to stimulation at five different wavelengths revealed an interaction between genotype and the response to stimulation at different wavelengths. Differences in VEP amplitudes and latencies were robust and barely impacted by the presence of the LRRK2 inhibitor PFE360, suggesting a developmental effect. Taken together, these results indicate that alterations in visual processing were related to developmental deficiency of LRRK2 and not acute deficiency of LRRK2, indicating a role of LRRK2 in the functional development of the visual system and synaptic transmission.