Evoked Potentials, Visual
Research Papers
EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI
Simultaneous EEG-fMRI is a powerful multimodal technique for imaging the brain, but its use in neurofeedback experiments has been limited by EEG noise caused by the MRI environment. Neurofeedback studies typically require analysis of EEG in real time, but EEG acquired inside the scanner is heavily contaminated with ballistocardiogram (BCG) artifact, a high-amplitude artifact locked to the cardiac cycle. Although techniques for removing BCG artifacts do exist, they are either not suited to real-time, low-latency applications, such as neurofeedback, or have limited efficacy. We propose and validate a new open-source artifact removal software called EEG-LLAMAS (Low Latency Artifact Mitigation Acquisition Software), which adapts and advances existing artifact removal techniques for low-latency experiments. We first used simulations to validate LLAMAS in data with known ground truth. We found that LLAMAS performed better than the best publicly-available real-time BCG removal technique, optimal basis sets (OBS), in terms of its ability to recover EEG waveforms, power spectra, and slow wave phase. To determine whether LLAMAS would be effective in practice, we then used it to conduct real-time EEG-fMRI recordings in healthy adults, using a steady state visual evoked potential (SSVEP) task. We found that LLAMAS was able to recover the SSVEP in real time, and recovered the power spectra collected outside the scanner better than OBS. We also measured the latency of LLAMAS during live recordings, and found that it introduced a lag of less than 50 ms on average. The low latency of LLAMAS, coupled with its improved artifact reduction, can thus be effectively used for EEG-fMRI neurofeedback. A limitation of the method is its use of a reference layer, a piece of EEG equipment which is not commercially available, but can be assembled in-house. This platform enables closed-loop experiments which previously would have been prohibitively difficult, such as those that target short-duration EEG events, and is shared openly with the neuroscience community.
View Full Paper →Early interaction between perceptual load and involuntary attention: An event-related potential study
Whether selective attention affects C1, the first (earliest) visual cortical component of the event-related potential (ERP), remains controversial. We used a cued, involuntary attention task requiring discrimination of targets under low and high levels of perceptual load to examine early attentional modulation in visual cortex. Potential confounds due to physical stimulus differences between load conditions and cue-target sensory interaction were minimized. An interaction between perceptual load and involuntary attention was observed for the P1m component (peak latency between 100 and 140 ms). Furthermore, the parieto-central C1 component (peak latency 80 ms) was modulated by attention, but only under the high-load condition. Thus, whereas attention typically modulates the later P1 component, attentional modulation of C1 is possible under optimal conditions. Specifically, a high perceptual load is necessary for eliciting this earliest attentional effect on cortical processing.
View Full Paper →Learning to control brain rhythms: making a brain-computer interface possible
The ability to control electroencephalographic rhythms and to map those changes to the actuation of mechanical devices provides the basis for an assistive brain-computer interface (BCI). In this study, we investigate the ability of subjects to manipulate the sensorimotor mu rhythm (8-12-Hz oscillations recorded over the motor cortex) in the context of a rich visual representation of the feedback signal. Four subjects were trained for approximately 10 h over the course of five weeks to produce similar or differential mu activity over the two hemispheres in order to control left or right movement in a three-dimensional video game. Analysis of the data showed a steep learning curve for producing differential mu activity during the first six training sessions and leveling off during the final four sessions. In contrast, similar mu activity was easily obtained and maintained throughout all the training sessions. The results suggest that an intentional BCI based on a binary signal is possible. During a realistic, interactive, and motivationally engaging task, subjects learned to control levels of mu activity faster when it involves similar activity in both hemispheres. This suggests that while individual control of each hemisphere is possible, it requires more learning time.
View Full Paper →Slow cortical potential biofeedback and the startle reflex
The negativity of slow cortical potentials (SCP) of the surface EEG is a measure of brain excitability, correlating with motor and cognitive preparation. Self-control of SCP positivity has been shown to reduce seizure activity. Following SCP biofeedback from a central EEG electrode position, subjects gained bidirectional control over their SCP. The current study used a modified feedback methodology, and found a positive relationship between negativity and magnitude of EMG startle response (a measure of cortical and subcortical arousal, particularly aversive response disposition). Greater success in SCP differentiation was associated with self-report of less relaxation during negativity training.
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