Cross-Task Consistency of Electroencephalography-Based Mental Workload Indicators: Comparisons Between Power Spectral Density and Task-Irrelevant Auditory Event-Related Potentials

Mental workload (MWL) estimators based on ongoing electroencephalography (EEG) and event-related potentials (ERPs) have shown great potentials to build adaptive aiding systems for human–machine systems by estimating MWL in real time. However, extracting EEG features which are consistent in indicating MWL across different tasks is still one of the critical challenges. This study attempts to compare the cross-task consistency in indexing MWL variations between two commonly used EEG-based MWL indicators, power spectral density (PSD) of ongoing EEG and task-irrelevant auditory ERPs (tir-aERPs). The verbal N-back and the multi-attribute task battery (MATB), both with two difficulty levels, were employed in the experiment, along with task-irrelevant auditory probes. EEG was recorded from 17 subjects when they were performing the tasks. The tir-aERPs elicited by the auditory probes and the relative PSDs of ongoing EEG between two consecutive auditory probes were extracted and statistically analyzed to reveal the effects of MWL and task type. Discriminant analysis and support vector machine were employed to examine the generalization of tir-aERP and PSD features in indexing MWL variations across different tasks. The results showed that the amplitudes of tir-aERP components, N1, early P3a, late P3a, and the reorienting negativity, significantly decreased with the increasing MWL in both N-back and MATB. Task type had no obvious influence on the amplitudes and topological layout of the MWL-sensitive tir-aERP features. The relative PSDs in θ, α, and low β bands were also sensitive to MWL variations. However, the MWL-sensitive PSD features and their topological patterns were significantly affected by task type. The cross-task classification results based on tir-aERP features also significantly outperformed the PSD features. These results suggest that the tir-aERPs should be potentially more consistent MWL indicators across very different task types when compared to PSD. The current study may provide new insights to our understanding of the common and distinctive neuropsychological essences of MWL across different tasks.

Does the phase of ongoing EEG oscillations predict auditory perception?

SummaryEffective processing of information from the environment requires the brain to selectively sample relevant inputs. The visual perceptual system has been shown to sample information rhythmically, oscillating rapidly between more and less input-favorable states. Evidence of parallel effects in auditory perception is inconclusive. Here, we combined a bilateral pitch-identification task with electroencephalography (EEG) to investigate whether the phase of ongoing EEG predicts auditory discrimination accuracy. We compared prestimulus phase distributions between correct and incorrect trials. Shortly before stimulus onset, each of these distributions showed significant phase concentration, but centered at different phase angles. The effects were strongest in theta and beta frequency bands. The divergence between phase distributions showed a linear relation with accuracy, accounting for at least 10% of inter-individual variance. Discrimination performance oscillated rhythmically at a rate predicted by the neural data. These findings indicate that auditory discrimination threshold oscillates over time along with the phase of ongoing EEG activity. Thus, it appears that auditory perception is discrete rather than continuous, with the phase of ongoing EEG oscillations shaping auditory perception by providing a temporal reference frame for information processing.

Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method that combined music information retrieval with spatial Independent Components Analysis (ICA) to probe the interplay between the spatial profiles and the spectral patterns. We projected the sensor data into cortical space using a minimum-norm estimate and applied the Short Time Fourier Transform (STFT) to obtain frequency information. Then, spatial ICA was made to extract spatial-spectral-temporal information of brain activity in source space and five long-term musical features were computationally extracted from the naturalistic stimuli. The spatial profiles of the components whose temporal courses were significantly correlated with musical feature time series were clustered to identify reproducible brain networks across the participants. Using the proposed approach, we found brain networks of musical feature processing are frequency-dependent and three plausible frequency-dependent networks were identified; the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.

Conservative method for attenuation of Vertical Electrooculogram based on local suppression of artifact templates from ongoing EEG

BackgroundEye movement during blinking can be a significant artifact in ERP analysis (mainly if ERP is blink-locked). Blinks produce a large positive potential in the vertical electrooculogram (VEOG), spreading towards posterior direction. Two methods are the most frequently used to suppress VEOGs: linear regression to subtract the VEOG signal from each EEG channel and Independent Component Analysis (ICA). However, both lose some EEG information.MethodsThe present algorithm (1) statistically identifies the time position of VEOGs in the frontopolar channels; (2) performs EEG averaging for each channel, which results in ‘blink templates’; (3) subtracts each template from the respective EEG at each VEOG position, only when the linear correlation index between the template and the segment is greater than a threshold L. The signals from twenty subjects were acquired using a behavioral test and were treated using FilterBlink for subsequent ERP analysis. A model was designed to test the method for each subject using twenty copies of the EEG signal from the mid-central channel of the subject (which has almost no VEOG) representing each one of the 20 EEG channels and their respective blink templates. At the same 200 equidistant time points (marks), a signal (2.5 sinusoidal cycles at 1050 ms to emulate an ERP) was mixed with each model channel, along with the respective blink template of that channel, between 500 to 1200 ms after each mark.ResultsAccording to the model, VEOGs interfered with both ERPs and the ongoing EEG mainly on the anterior medial leads, and no significant effect was observed on the mid-central channel (Cz). FilterBlink recovered approximately 90% (at Fp1) to 98% (Fz) of the original ERP and EEG signals to L of 0.1. In the analysis of real signals, the method reduced drastically the VEOG effect on the EEG after ERP and blink-artifact averaging.ConclusionThe method is very simple and effective for VEOG attenuation without significant distortion of the EEG signal and embedded ERPs in all channels.