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 thalamocortical motor circuits. However, the STN is also anatomically connected to other thalamocortical 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. Here we 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 behaviour), 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 1 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 (>60 Hz) activity in the STN. Trial-to-trial modelling 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 peri-surgical recordings.

Filter bank temporally local multivariate synchronization index for SSVEP-based BCI.

BACKGROUND: Multivariate synchronization index (MSI) has been successfully applied for frequency detection in steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems. However, the standard MSI algorithm and its variants cannot simultaneously take full advantage of the time-local structure and the harmonic components in SSVEP signals, which are both crucial for frequency detection performance. To overcome the limitation, we propose a novel filter bank temporally local MSI (FBTMSI) algorithm to further improve SSVEP frequency detection accuracy. The method explicitly utilizes the temporal information of signal for covariance matrix estimation and employs filter bank decomposition to exploits SSVEP-related harmonic components. RESULTS: We employed the cross-validation strategy on the public Benchmark dataset to optimize the parameters and evaluate the performance of the FBTMSI algorithm. Experimental results show that FBTMSI outperforms the standard MSI, temporally local MSI (TMSI) and filter bank driven MSI (FBMSI) algorithms across multiple experimental settings. In the case of data length of one second, the average accuracy of FBTMSI is 9.85% and 3.15% higher than that of the FBMSI and the TMSI, respectively. CONCLUSIONS: The promising results demonstrate the effectiveness of the FBTMSI algorithm for frequency recognition and show its potential in SSVEP-based BCI applications.

Listen to your heart: Trade-off between cardiac interoceptive processing and visual exteroceptive processing.

Internal bodily signals, such as heartbeats, can influence conscious perception of external sensory information. Spontaneous shifts of attention between interoception and exteroception have been proposed as the underlying mechanism, but direct evidence is lacking. Here, we used steady-state visual evoked potential (SSVEP) frequency tagging to independently measure the neural processing of visual stimuli that were concurrently presented but varied in heartbeat coupling in healthy participants. Although heartbeat coupling was irrelevant to participants' task of detecting brief color changes, we found decreased SSVEPs for systole-coupled stimuli and increased SSVEPs for diastole-coupled stimuli, compared to non-coupled stimuli. These results suggest that attentional and representational resources allocated to visual stimuli vary according to fluctuations in cardiac-related signals across the cardiac cycle, reflecting spontaneous and immediate competition between cardiac-related signals and visual events. Furthermore, frequent coupling of visual stimuli with stronger cardiac-related signals not only led to a larger heartbeat evoked potential (HEP) but also resulted in a smaller color change evoked N2 component, with the increase in HEP amplitude associated with a decrease in N2 amplitude. These findings indicate an overall or longer-term increase in brain resources allocated to the internal domain at the expense of reduced resources available for the external domain. Our study highlights the dynamic reallocation of limited processing resources across the internal-external axis and supports the trade-off between interoception and exteroception.

An improved SSVEP-based brain-computer interface with low-contrast visual stimulation and its application in UAV control.

Efficient communication and regulation are crucial for advancing brain-computer interfaces (BCIs), with the steady-state visual-evoked potential (SSVEP) paradigm demonstrating high accuracy and information transfer rates. However, the conventional SSVEP paradigm encounters challenges related to visual occlusion and fatigue. In this study, we propose an improved SSVEP paradigm that addresses these issues by lowering the contrast of visual stimulation. The improved paradigms outperform the traditional paradigm in the experiments, significantly reducing the visual stimulation of the SSVEP paradigm. Furthermore, we apply this enhanced paradigm to a BCI navigation system, enabling two-dimensional navigation of unmanned aerial vehicles (UAVs) through a first-person perspective. Experimental results indicate the enhanced SSVEP-based BCI system's accuracy in performing navigation and search tasks. Our findings highlight the feasibility of the enhanced SSVEP paradigm in mitigating visual occlusion and fatigue issues, presenting a more intuitive and natural approach for BCIs to control external equipment.NEW & NOTEWORTHY In this article, we proposed an improved steady-state visual-evoked potential (SSVEP) paradigm and constructed an SSVEP-based brain-computer interface (BCI) system to navigate the unmanned aerial vehicle (UAV) in two-dimensional (2-D) physical space. We proposed a modified method for evaluating visual fatigue including subjective score and objective indices. The results indicated that the improved SSVEP paradigm could effectively reduce visual fatigue while maintaining high accuracy.

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

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

In-Car Environment Control Using an SSVEP-Based Brain-Computer Interface with Visual Stimuli Presented on Head-Up Display: Performance Comparison with a Button-Press Interface.

Controlling the in-car environment, including temperature and ventilation, is necessary for a comfortable driving experience. However, it often distracts the driver's attention, potentially causing critical car accidents. In the present study, we implemented an in-car environment control system utilizing a brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). In the experiment, four visual stimuli were displayed on a laboratory-made head-up display (HUD). This allowed the participants to control the in-car environment by simply staring at a target visual stimulus, i.e., without pressing a button or averting their eyes from the front. The driving performances in two realistic driving tests-obstacle avoidance and car-following tests-were then compared between the manual control condition and SSVEP-BCI control condition using a driving simulator. In the obstacle avoidance driving test, where participants needed to stop the car when obstacles suddenly appeared, the participants showed significantly shorter response time (1.42 ± 0.26 s) in the SSVEP-BCI control condition than in the manual control condition (1.79 ± 0.27 s). No-response rate, defined as the ratio of obstacles that the participants did not react to, was also significantly lower in the SSVEP-BCI control condition (4.6 ± 14.7%) than in the manual control condition (20.5 ± 25.2%). In the car-following driving test, where the participants were instructed to follow a preceding car that runs at a sinusoidally changing speed, the participants showed significantly lower speed difference with the preceding car in the SSVEP-BCI control condition (15.65 ± 7.04 km/h) than in the manual control condition (19.54 ± 11.51 km/h). The in-car environment control system using SSVEP-based BCI showed a possibility that might contribute to safer driving by keeping the driver's focus on the front and thereby enhancing the overall driving performance.

[The supernumerary robotic limbs of brain-computer interface based on asynchronous steady-state visual evoked potential].

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) have attracted much attention in the field of intelligent robotics. Traditional SSVEP-based BCI systems mostly use synchronized triggers without identifying whether the user is in the control or non-control state, resulting in a system that lacks autonomous control capability. Therefore, this paper proposed a SSVEP asynchronous state recognition method, which constructs an asynchronous state recognition model by fusing multiple time-frequency domain features of electroencephalographic (EEG) signals and combining with a linear discriminant analysis (LDA) to improve the accuracy of SSVEP asynchronous state recognition. Furthermore, addressing the control needs of disabled individuals in multitasking scenarios, a brain-machine fusion system based on SSVEP-BCI asynchronous cooperative control was developed. This system enabled the collaborative control of wearable manipulator and robotic arm, where the robotic arm acts as a "third hand", offering significant advantages in complex environments. The experimental results showed that using the SSVEP asynchronous control algorithm and brain-computer fusion system proposed in this paper could assist users to complete multitasking cooperative operations. The average accuracy of user intent recognition in online control experiments was 93.0%, which provides a theoretical and practical basis for the practical application of the asynchronous SSVEP-BCI system.

[Visual object detection system based on augmented reality and steady-state visual evoked potential].

This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects' brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.

Laplacian reference is optimal for steady-state visual-evoked potentials.

Steady-state visual-evoked potentials (SSVEPs) are widely used in human neuroscience studies and applications such as brain-computer interfaces (BCIs). Surprisingly, no previous study has systematically evaluated different reference methods for SSVEP analysis, despite that signal reference is crucial for the proper assessment of neural activities. In the present study, using four datasets from our previous SSVEP studies (Chen J, Valsecchi M, Gegenfurtner KR. J Neurophysiol 118: 749-754, 2017; Chen J, Valsecchi M, Gegenfurtner KR. Neuropsychologia 102: 206-216, 2017; Chen J, McManus M, Valsecchi M, Harris LR, Gegenfurtner KR. J Vis 19: 8, 2019) and three public datasets from other studies (Baker DH, Vilidaite G, Wade AR. PLoS Comput Biol 17: e1009507, 2021; Lygo FA, Richard B, Wade AR, Morland AB, Baker DH. NeuroImage 230: 117780, 2021; Vilidaite G, Norcia AM, West RJH, Elliott CJH, Pei F, Wade AR, Baker DH. Proc R Soc B 285: 20182255, 2018), we compared four reference methods: monopolar reference, common average reference, averaged-mastoids reference, and Laplacian reference. The quality of the resulting SSVEP signals was compared in terms of both signal-to-noise ratios (SNRs) and reliability. The results showed that Laplacian reference, which uses signals at the maximally activated electrode after subtracting the average of the nearby electrodes to reduce common noise, gave rise to the highest SNRs. Furthermore, the Laplacian reference resulted in SSVEP signals that were highly reliable across recording sessions or trials. These results suggest that Laplacian reference is optimal for SSVEP studies and applications. Laplacian reference is especially advantageous for SSVEP experiments where short preparation time is preferred as it requires only data from the maximally activated electrode and a few surrounding electrodes.NEW & NOTEWORTHY The present study provides a comprehensive evaluation of the use of different reference methods for steady-state visual-evoked potentials (SSVEPs) and has found that Laplacian reference increases signal-to-noise ratios (SNRs) and enhances reliabilities of SSVEP signals. Thus, the results suggest that Laplacian reference is optimal for SSVEP analysis.

Sustained selective attention to chromatic information enhances visuocortical gain at the population level.

Prior work in selective attention research has shown that colour-selective attention enhances neural activity in visuocortical areas sensitive to the attended colour while suppressing activity in areas sensitive to ignored colours. However, it is currently unclear whether this effect is limited to attending to specific colour hues or extends to chromatic information more broadly. To investigate this question, we used steady-state visual evoked potentials (ssVEPs) frequency tagging to quantify participants' visuocortical responses to specific elements embedded in arrays of flickering, randomly moving mid-complex patterns. Participants were instructed to attend to either coloured or greyscale patterns while ignoring the others. We found that attending to either coloured or greyscale patterns produced robust increases in ssVEP amplitudes both compared to ignored stimuli and to baseline. There was however no evidence of suppressed responses to ignored patterns. These findings demonstrate that attentional selection based on the presence or absence of chromatic information prompts selectively enhanced visuocortical processing but this selective amplification is not accompanied by suppression of unattended stimuli. Findings are consistent with theoretical notions that predict strong competition between specific exemplars within a given feature dimension, such as red or green, but weak competition between broadly defined stimulus categories, such as chromatic versus non-chromatic.

Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control.

Brain-computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.

Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm.

Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems have been extensively researched over the past two decades, and multiple sets of standard datasets have been published and widely used. However, there are differences in sample distribution and collection equipment across different datasets, and there is a lack of a unified evaluation method. Most new SSVEP decoding algorithms are tested based on self-collected data or offline performance verification using one or two previous datasets, which can lead to performance differences when used in actual application scenarios. To address these issues, this paper proposed a SSVEP dataset evaluation method and analyzed six datasets with frequency and phase modulation paradigms to form an SSVEP algorithm evaluation dataset system. Finally, based on the above datasets, performance tests were carried out on the four existing SSVEP decoding algorithms. The findings reveal that the performance of the same algorithm varies significantly when tested on diverse datasets. Substantial performance variations were observed among subjects, ranging from the best-performing to the worst-performing. The above results demonstrate that the SSVEP dataset evaluation method can integrate six datasets to form a SSVEP algorithm performance testing dataset system. This system can test and verify the SSVEP decoding algorithm from different perspectives such as different subjects, different environments, and different equipment, which is helpful for the research of new SSVEP decoding algorithms and has significant reference value for other BCI application fields.

Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals.

Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain's responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus.

[Recognition of high-frequency steady-state visual evoked potential for brain-computer interface].

Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.

[Advances in brain-computer interface based on high-frequency steady-state visual evoked potential].

Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.

Aversive Conditioning of Spatial Position Sharpens Neural Population-Level Tuning in Visual Cortex and Selectively Alters Alpha-Band Activity.

Processing capabilities for many low-level visual features are experientially malleable, aiding sighted organisms in adapting to dynamic environments. Explicit instructions to attend a specific visual field location influence retinotopic visuocortical activity, amplifying responses to stimuli appearing at cued spatial positions. It remains undetermined both how such prioritization affects surrounding nonprioritized locations, and if a given retinotopic spatial position can attain enhanced cortical representation through experience rather than instruction. The current report examined visuocortical response changes as human observers (N = 51, 19 male) learned, through differential classical conditioning, to associate specific screen locations with aversive outcomes. Using dense-array EEG and pupillometry, we tested the preregistered hypotheses of either sharpening or generalization around an aversively associated location following a single conditioning session. Competing hypotheses tested whether mean response changes would take the form of a Gaussian (generalization) or difference-of-Gaussian (sharpening) distribution over spatial positions, peaking at the viewing location paired with a noxious noise. Occipital 15 Hz steady-state visual evoked potential responses were selectively heightened when viewing aversively paired locations and displayed a nonlinear, difference-of-Gaussian profile across neighboring locations, consistent with suppressive surround modulation of nonprioritized positions. Measures of alpha-band (8-12 Hz) activity were differentially altered in anterior versus posterior locations, while pupil diameter exhibited selectively heightened responses to noise-paired locations but did not evince differences across the nonpaired locations. These results indicate that visuocortical spatial representations are sharpened in response to location-specific aversive conditioning, while top-down influences indexed by alpha-power reduction exhibit posterior generalization and anterior sharpening.SIGNIFICANCE STATEMENT It is increasingly recognized that early visual cortex is not a static processor of physical features, but is instead constantly shaped by perceptual experience. It remains unclear, however, to what extent the cortical representation of many fundamental features, including visual field location, is malleable by experience. Using EEG and an aversive classical conditioning paradigm, we observed sharpening of visuocortical responses to stimuli appearing at aversively associated locations along with location-selective facilitation of response systems indexed by pupil diameter and EEG alpha power. These findings highlight the experience-dependent flexibility of retinotopic spatial representations in visual cortex, opening avenues toward novel treatment targets in disorders of attention and spatial cognition.

Steady-state visual evoked potentials differentiate between internally and externally directed attention.

While attention to external visual stimuli has been extensively studied, attention directed internally towards mental contents (e.g., thoughts, memories) or bodily signals (e.g., breathing, heartbeat) has only recently become a subject of increased interest, due to its relation to interoception, contemplative practices and mental health. The present study aimed at expanding the methodological toolbox for studying internal attention, by examining for the first time whether the steady-state visual evoked potential (ssVEP), a well-established measure of attention, can differentiate between internally and externally directed attention. To this end, we designed a task in which flickering dots were used to generate ssVEPs, and instructed participants to count visual targets (external attention condition) or their heartbeats (internal attention condition). We compared the ssVEP responses between conditions, along with alpha-band activity and the heartbeat evoked potential (HEP) - two electrophysiological measures associated with internally directed attention. Consistent with our hypotheses, we found that both the magnitude and the phase synchronization of the ssVEP decreased when attention was directed internally, suggesting that ssVEP measures are able to differentiate between internal and external attention. Additionally, and in line with previous findings, we found larger suppression of parieto-occipital alpha-band activity and an increase of the HEP amplitude in the internal attention condition. Furthermore, we found a trade-off between changes in ssVEP response and changes in HEP and alpha-band activity: when shifting from internal to external attention, increase in ssVEP response was related to a decrease in parieto-occipital alpha-band activity and HEP amplitudes. These findings suggest that shifting between external and internal directed attention prompts a re-allocation of limited processing resources that are shared between external sensory and interoceptive processing.

Dynamics of attentional allocation to targets and distractors during visual search.

There is much debate about the neural mechanisms that achieve suppression of salient distracting stimuli during visual search. The proactive suppression hypothesis asserts that if exposed to the same distractors repeatedly, these stimuli are actively inhibited before attention can be shifted to them. A contrasting proposal holds that attention is initially captured by salient distractors but is subsequently withdrawn. By concurrently measuring stimulus-driven and intrinsic brain potentials in 36 healthy human participants, we obtained converging evidence against early proactive suppression of distracting input. Salient distractors triggered negative event-related potentials (N1pc/N2pc), enhanced the steady-state visual evoked potential (SSVEP) relative to non-salient (filler) stimuli, and suppressed contralateral relative to ipsilateral alpha-band amplitudes-three electrophysiological measure associated with the allocation of attention-even though these distractors did not interfere with behavioral responses to the search targets. Furthermore, these measures indicated that both stimulus-driven and goal-driven allocations of attention occurred in conjunction with one another, with the goal-driven effect enhancing and prolonging the stimulus-driven effect. These results provide a new perspective on the traditional dichotomy between bottom-up and top-down attentional allocation. Control experiments revealed that continuous marking of the locations at which the search display items were presented resulted in a dramatic and unexpected conversion of the target-elicited N2pc into a shorter-latency N1pc in association with faster reaction times to the targets.

Unexpected Sounds Nonselectively Inhibit Active Visual Stimulus Representations.

The brain's capacity to process unexpected events is key to cognitive flexibility. The most well-known effect of unexpected events is the interruption of attentional engagement (distraction). We tested whether unexpected events interrupt attentional representations by activating a neural mechanism for inhibitory control. This mechanism is most well characterized within the motor system. However, recent work showed that it is automatically activated by unexpected events and can explain some of their nonmotor effects (e.g., on working memory representations). Here, human participants attended to lateralized flickering visual stimuli, producing steady-state visual evoked potentials (SSVEPs) in the scalp electroencephalogram. After unexpected sounds, the SSVEP was rapidly suppressed. Using a functional localizer (stop-signal) task and independent component analysis, we then identified a fronto-central EEG source whose activity indexes inhibitory motor control. Unexpected sounds in the SSVEP task also activated this source. Using single-trial analyses, we found that subcomponents of this source differentially relate to sound-induced SSVEP changes: While its N2 component predicted the subsequent suppression of the attended-stimulus SSVEP, the P3 component predicted the suppression of the SSVEP to the unattended stimulus. These results shed new light on the processes underlying fronto-central control signals and have implications for phenomena such as distraction and the attentional blink.

Practical real-time MEG-based neural interfacing with optically pumped magnetometers.

BACKGROUND: Brain-computer interfaces decode intentions directly from the human brain with the aim to restore lost functionality, control external devices or augment daily experiences. To combine optimal performance with wide applicability, high-quality brain signals should be captured non-invasively. Magnetoencephalography (MEG) is a potent candidate but currently requires costly and confining recording hardware. The recently developed optically pumped magnetometers (OPMs) promise to overcome this limitation, but are currently untested in the context of neural interfacing. RESULTS: In this work, we show that OPM-MEG allows robust single-trial analysis which we exploited in a real-time 'mind-spelling' application yielding an average accuracy of 97.7%. CONCLUSIONS: This shows that OPM-MEG can be used to exploit neuro-magnetic brain responses in a practical and flexible manner, and opens up new avenues for a wide range of new neural interface applications in the future.