Real-time EEG Source-mapping Toolbox (REST): Online ICA and source localization

The Electroencephalogram (EEG) is a noninvasive functional brain activity recording method that shows promise for becoming a 3-D cortical imaging modality with high temporal resolution. Currently, most of the tools developed for EEG analysis focus mainly on offline processing. This study introduces and demonstrates the Real-time EEG Source-mapping Toolbox (REST), an extension to the widely distributed EEGLAB environment. REST allows blind source separation of EEG data in real-time using Online Recursive Independent Component Analysis (ORICA), plus near real-time localization of separated sources. Two source localization methods are available to fit equivalent current dipoles or estimate spatial source distributions of selected sources. Selected measures of raw EEG data or component activations (e.g. time series of the data, spectral changes over time, equivalent current dipoles, etc.) can be visualized in near real-time. Finally, this study demonstrates the accuracy and functionality of REST with data from two experiments and discusses some relevant applications.

Tracking non-stationary EEG sources using adaptive online recursive independent component analysis

Electroencephalographic (EEG) source-level analyses such as independent component analysis (ICA) have uncovered features related to human cognitive functions or artifactual activities. Among these methods, Online Recursive ICA (ORICA) has been shown to achieve fast convergence in decomposing high-density EEG data for real-time applications. However, its adaptation performance has not been fully explored due to the difficulty in choosing an appropriate forgetting factor: the weight applied to new data in a recursive update which determines the trade-off between the adaptation capability and convergence quality. This study proposes an adaptive forgetting factor for ORICA (adaptive ORICA) to learn and adapt to non-stationarity in the EEG data. Using a realistically simulated non-stationary EEG dataset, we empirically show adaptive forgetting factors outperform other commonly-used non-adaptive rules when underlying source dynamics are changing. Standard offline ICA can only extract a subset of the changing sources while adaptive ORICA can recover all. Applied to actual EEG data recorded from a task-switching experiments, adaptive ORICA can learn and re-learn the task-related components as they change. With an adaptive forgetting factor, adaptive ORICA can track non-stationary EEG sources, opening many new online applications in brain-computer interfaces and in monitoring of brain dynamics.

Transfer learning with large-scale data in brain-computer interfaces

Human variability in electroencephalogram (EEG) poses significant challenges for developing practical real-world applications of brain-computer interfaces (BCIs). The intuitive solution of collecting sufficient user-specific training/calibration data can be very labor-intensive and time-consuming, hindering the practicability of BCIs. To address this problem, transfer learning (TL), which leverages existing data from other sessions or subjects, has recently been adopted by the BCI community to build a BCI for a new user with limited calibration data. However, current TL approaches still require training/calibration data from each of conditions, which might be difficult or expensive to obtain. This study proposed a novel TL framework that could nearly eliminate requirement of subject-specific calibration data by leveraging large-scale data from other subjects. The efficacy of this method was validated in a passive BCI that was designed to detect neurocognitive lapses during driving. With the help of large-scale data, the proposed TL approach outperformed the within-subject approach while considerably reducing the amount of calibration data required for each individual (~1.5 min of data from each individual as opposed to a 90 min pilot session used in a standard within-subject approach). This demonstration might considerably facilitate the real-world applications of BCIs.

An Online Brain-Computer Interface Based on SSVEPs Measured From Non-Hair-Bearing Areas

Steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has gained a lot of attention due to its robustness and high information transfer rate (ITR). However, transitioning well-controlled laboratory-oriented BCI demonstrations to real-world applications poses severe challenges for this exciting field. For instance, conducting BCI experiments usually requires skilled technicians to abrade the area of skin underneath each electrode and apply an electrolytic gel or paste to acquire high-quality SSVEPs from hair-covered areas. Our previous proof-of-concept study has proposed an alternative approach that employed electroencephalographic signals collected from easily accessible non-hair-bearing areas including neck, behind the ears, and face to realize an SSVEP-based BCI. The study results showed that, with proper electrode placements and advanced signal-processing algorithms, the SSVEPs measured from non-hair-bearing areas in off-line SSVEP experiments could achieve comparable SNR to that obtained from the hair-bearing occipital areas. This study extended the previous work to systematically investigate the costs and benefits of non-hair SSVEPs. Furthermore, this study developed and evaluated an online BCI system based solely on non-hair EEG signals. A 12-target identification task was employed to quantitatively assess the performance of the online SSVEP-based BCI system. All subjects successfully completed the tasks using non-hair SSVEPs with 84.08 ± 15.60% averaged accuracy and 30.21 ± 10.61 bits/min averaged ITR. The empirical results of this study demonstrated the practicality of implementing an SSVEP-based BCI based on signals from non-hair-bearing areas, significantly improving the feasibility and practicality of real-world BCIs.

An EEG-Based Fatigue Detection and Mitigation System

Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual’s internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant’s EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha- and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject’s cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.

Mind-Wandering Tends to Occur under Low Perceptual Demands during Driving

Fluctuations in attention behind the wheel poses a significant risk for driver safety. During transient periods of inattention, drivers may shift their attention towards internally-directed thoughts or feelings at the expense of staying focused on the road. This study examined whether increasing task difficulty by manipulating involved sensory modalities as the driver detected the lane-departure in a simulated driving task would promote a shift of brain activity between different modes of processing, reflected by brain network dynamics on electroencephalographic sources. Results showed that depriving the driver of salient sensory information imposes a relatively more perceptually-demanding task, leading to a stronger activation in the task-positive network. When the vehicle motion feedback is available, the drivers may rely on vehicle motion to perceive the perturbations, which frees attentional capacity and tends to activate the default mode network. Such brain network dynamics could have major implications for understanding fluctuations in driver attention and designing advance driver assistance systems.

Toward non-hair-bearing brain-computer interfaces for neurocognitive lapse detection

Recent advances in mobile electroencephalogram (EEG) acquisition based on dry electrodes have started moving Brain-Computer Interface (BCI) applications from well-controlled laboratory settings to real-world environments. However, the application mechanisms and high impedance of dry electrodes over the hair-covered areas remain challenging for everyday use of BCI. In addition, whole-scalp recordings are not always necessary or applicable due to various practical constrains. Therefore, alternative montages for EEG recordings to meet the everyday needs are in-demand. Inspired by our previous work on measuring non-hair-bearing steady state visual evoked potentials for BCI applications, this study explores the feasibility and efficacy of detecting cognitive lapses of participants based on EEG signals collected from the non-hair-bearing areas. Study results suggest that informative EEG features associated with lapses could be assessed from non-hair-bearing areas with comparable accuracy obtained from the whole-scalp EEG. The design principles, validation processes and promising findings reported in this study may enable and/or facilitate numerous BCI applications in real-world environments.

Developing an online steady-state visual evoked potential-based brain-computer interface system using EarEEG

The purpose of this study is to demonstrate an online steady-state visual evoked potential (SSVEP)-based BCI system using EarEEG. EarEEG is a novel recording concept where electrodes are embedded on the surface of earpieces customized to the individual anatomical shape of users' ear. It has been shown that the EarEEG can be used to record SSVEPs in previous studies. However, a long distance between the visual cortex and the ear makes the signal-to-noise ratio (SNR) of SSVEPs acquired by the EarEEG relatively low. Recently, filter bank- and training data-based canonical correlation analysis algorithms have shown significant performance improvement in terms of accuracy of target detection and information transfer rate (ITR). This study implemented an online four-class SSVEP-based BCI system using EarEEG. Four subjects participated in offline and online BCI experiments. For the offline classification, an average accuracy of 82.71±11.83 % was obtained using 4 sec-long SSVEPs acquired from earpieces. In the online experiment, all subjects successfully completed the tasks with an average accuracy of 87.92±12.10 %, leading to an average ITR of 16.60±6.55 bits/min. The results suggest that EarEEG can be used to perform practical BCI applications. The EarEEG has the potential to be used as a portable EEG recordings platform, that could enable real-world BCI applications.

Real-Time Adaptive EEG Source Separation Using Online Recursive Independent Component Analysis

Independent component analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and validate an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data. Empirical results of this study demonstrate the algorithm's: 1) suitability for accurate and efficient source identification in high-density (64-channel) realistically-simulated EEG data; 2) capability to detect and adapt to nonstationarity in 64-ch simulated EEG data; and 3) utility for rapidly extracting principal brain and artifact sources in real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment. ORICA was implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces.

Hybrid frequency and phase coding for a high-speed SSVEP-based BCI speller

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize high-speed communication between the human brain and the external environment. Recently, multiple access (MA) methods in telecommunications have been introduced into the system design of BCIs and showed their potential in improving BCI performance. This study investigated the feasibility of hybrid frequency and phase coding methods in multi-target SSVEP-based BCIs. Specifically, this study compared two hybrid target-coding strategies: (1) mixed frequency and phase coding, and (2) joint frequency and phase coding. In a simulated online BCI experiment using a 40-target BCI speller, BCI performance for both coding approaches were tested with a group of six subjects. At a spelling speed of 40 characters per minute (1.5 seconds per character), both approaches obtained high information transfer rates (ITR) (mixed coding: 172.37±28.67 bits/min, joint coding: 170.94±28.32 bits/min) across subjects. There was no statistically significant difference between the two approaches (p>0.05). These results suggest that the hybrid frequency and phase coding methods are highly efficient for multi-target coding in SSVEP BCIs with a large number of classes, providing a practical solution to implement a high-speed BCI speller.

An efficient ASIC implementation of 16-channel on-line recursive ICA processor for real-time EEG system

This is a proposal for an efficient very-large-scale integration (VLSI) design, 16-channel on-line recursive independent component analysis (ORICA) processor ASIC for real-time EEG system, implemented with TSMC 40 nm CMOS technology. ORICA is appropriate to be used in real-time EEG system to separate artifacts because of its highly efficient and real-time process features. The proposed ORICA processor is composed of an ORICA processing unit and a singular value decomposition (SVD) processing unit. Compared with previous work [1], this proposed ORICA processor has enhanced effectiveness and reduced hardware complexity by utilizing a deeper pipeline architecture, shared arithmetic processing unit, and shared registers. The 16-channel random signals which contain 8-channel super-Gaussian and 8-channel sub-Gaussian components are used to analyze the dependence of the source components, and the average correlation coefficient is 0.95452 between the original source signals and extracted ORICA signals. Finally, the proposed ORICA processor ASIC is implemented with TSMC 40 nm CMOS technology, and it consumes 15.72 mW at 100 MHz operating frequency.

Enhancing detection of steady-state visual evoked potentials using individual training data

Although the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has improved gradually in the past decades, it still does not meet the requirement of a high communication speed in many applications. A major challenge is the interference of spontaneous background EEG activities in discriminating SSVEPs. An SSVEP BCI using frequency coding typically does not have a calibration procedure since the frequency of SSVEPs can be recognized by power spectrum density analysis (PSDA). However, the detection rate can be deteriorated by the spontaneous EEG activities within the same frequency range because phase information of SSVEPs is ignored in frequency detection. To address this problem, this study proposed to incorporate individual SSVEP training data into canonical correlation analysis (CCA) to improve the frequency detection of SSVEPs. An eight-class SSVEP dataset recorded from 10 subjects in a simulated online BCI experiment was used for performance evaluation. Compared to the standard CCA method, the proposed method obtained significantly improved detection accuracy (95.2% vs. 88.4%, p<0.05) and information transfer rates (ITR) (104.6 bits/min vs. 89.1 bits/min, p<0.05). The results suggest that the employment of individual SSVEP training data can significantly improve the detection rate and thereby facilitate the implementation of a high-speed BCI.

Enhancing unsupervised canonical correlation analysis-based frequency detection of SSVEPs by incorporating background EEG

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to provide a fast communication channel between human brain and external devices. In SSVEP-based BCIs, Canonical Correlation Analysis (CCA) has been widely used to detect frequency-coded SSVEPs due to its high efficiency and robustness. However, the detectability of SSVEPs differs among frequencies due to a power-law distribution of the power spectra of spontaneous electroencephalogram (EEG) signals. This study proposed a new method based on the fact that changes of canonical correlation coefficients for SSVEPs and background EEG signals follow the same trend along frequency. The proposed method defined a normalized canonical correlation coefficient, the ratio of the canonical correlation coefficient for SSVEPs to the mean of the canonical correlation coefficients for background EEG signals, to enhance the frequency detection of SSVEPs. An SSVEP dataset from 13 subjects was used for comparing classification performance between the proposed method and the standard CCA method. Classification accuracy and simulated information transfer rates (ITR) suggest that, in an unsupervised way, the proposed method could considerably improve the frequency detection accuracy of SSVEPs with little computational effort.

Detecting glaucomatous change in visual fields: Analysis with an optimization framework

Detecting glaucomatous progression is an important aspect of glaucoma management. The assessment of longitudinal series of visual fields, measured using Standard Automated Perimetry (SAP), is considered the reference standard for this effort. We seek efficient techniques for determining progression from longitudinal visual fields by formulating the problem as an optimization framework, learned from a population of glaucoma data. The longitudinal data from each patient's eye were used in a convex optimization framework to find a vector that is representative of the progression direction of the sample population, as a whole. Post-hoc analysis of longitudinal visual fields across the derived vector led to optimal progression (change) detection. The proposed method was compared to recently described progression detection methods and to linear regression of instrument-defined global indices, and showed slightly higher sensitivities at the highest specificities than other methods (a clinically desirable result). The proposed approach is simpler, faster, and more efficient for detecting glaucomatous changes, compared to our previously proposed machine learning-based methods, although it provides somewhat less information. This approach has potential application in glaucoma clinics for patient monitoring and in research centers for classification of study participants.

Real-Time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG

GOAL

We present and evaluate a wearable high-density dry-electrode EEG system and an open-source software framework for online neuroimaging and state classification.

METHODS

The system integrates a 64-channel dry EEG form factor with wireless data streaming for online analysis. A real-time software framework is applied, including adaptive artifact rejection, cortical source localization, multivariate effective connectivity inference, data visualization, and cognitive state classification from connectivity features using a constrained logistic regression approach (ProxConn). We evaluate the system identification methods on simulated 64-channel EEG data. Then, we evaluate system performance, using ProxConn and a benchmark ERP method, in classifying response errors in nine subjects using the dry EEG system.

RESULTS

Simulations yielded high accuracy (AUC = 0.97 ± 0.021) for real-time cortical connectivity estimation. Response error classification using cortical effective connectivity [short-time direct-directed transfer function (sdDTF)] was significantly above chance with similar performance (AUC) for cLORETA (0.74 ±0.09) and LCMV (0.72 ±0.08) source localization. Cortical ERP-based classification was equivalent to ProxConn for cLORETA (0.74 ±0.16) but significantly better for LCMV (0.82 ±0.12) .

CONCLUSION

We demonstrated the feasibility for real-time cortical connectivity analysis and cognitive state classification from high-density wearable dry EEG.

SIGNIFICANCE

This paper is the first validated application of these methods to 64-channel dry EEG. This study addresses a need for robust real-time measurement and interpretation of complex brain activity in the dynamic environment of the wearable setting. Such advances can have broad impact in research, medicine, and brain-computer interfaces. The pipelines are made freely available in the open-source SIFT and BCILAB toolboxes.

An efficient VLSI implementation of on-line recursive ICA processor for real-time multi-channel EEG signal separation

This paper presents an efficient VLSI implementation of on-line recursive ICA (ORICA) processor for real-time multi-channel EEG signal separation. The proposed design contains a system control unit, a whitening unit, a singular value decomposition unit, a floating matrix multiply unit and, and an ORICA weight training unit. Because the input sample rate of the ORICA processor is 128 Hz, the ORICA processor should produce independent components before the next sample is input in 1/128 s. Under the timing constraints of commutating multi-channel ORICA in real time, the design of the ORICA processor is a mixed architecture, which is designed as different hardware parallelism according to the complexity of processing units. The shared arithmetic processing unit and shared register can reduce hardware complexity and power consumption. The proposed design is implemented used TSMC 90 nm CMOS technology with 8-channel EEG processing in 128 Hz sample rate of raw data and consumes 2.827 mW at 50 MHz clock rate. The performance of the proposed design is also shown to reach 0.0078125 s latency after each EEG sample time, and the average correlation coefficient between the original source signals and extracted ORICA signals for each 1 s frame is 0.9763.

Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG

This report summarizes our recent efforts to deliver real-time data extraction, preprocessing, artifact rejection, source reconstruction, multivariate dynamical system analysis (including spectral Granger causality) and 3D visualization as well as classification within the open-source SIFT and BCILAB toolboxes. We report the application of such a pipeline to simulated data and real EEG data obtained from a novel wearable high-density (64-channel) dry EEG system.

Automatic design for independent component analysis based brain-computer interfacing

This study proposes a new framework, independent component ensemble, to leverage the acquired knowledge into a truly automatic and on-line EEG-based brain-computer interfacing (BCI). The envisioned design includes: (1) independent source recover using independent component analysis (ICA) (2) automatic selection of the independent components of interest (ICi) associated with human behaviors; (3) multiple classifiers with a parallel constructing and processing structure; and (4) a simple fusion scheme to combine the decisions from multiple classifiers. Its implications in BCI are demonstrated through a sample application: cognitive-state monitoring of participants performing a realistic sustained-attention driving task. Empirical results showed the proposed ensemble design could provide an improvement of 7% ~ 15% in overall accuracy for the classification of the arousal state and the driving performance. In summary, constructing ICi-ensemble classifiers and combining their outputs demonstrates a practical option for ICA-based BCIs to reduce the risk of not obtaining any desired independent source or selecting an inadequate component. Most importantly, the ensemble design for integrating information across multiple brain areas creates potentials for developing more complicated BCIs for real world applications.

Developing stimulus presentation on mobile devices for a truly portable SSVEP-based BCI

This study integrates visual stimulus presentation and near real-time data processing on a mobile device (e.g. a Tablet or a cell-phone) to implement a steady-state visual evoked potentials (SSVEP)-based brain-computer interface (BCI). The goal of this study is to increase the practicability, portability and ubiquity of an SSVEP-based BCI for daily use. The accuracy of flickering frequencies on the mobile SSVEP BCI system was tested against that on a laptop/desktop used in our previous studies. This study then analyzed the power spectrum density of the electroencephalogram signals elicited by the visual stimuli rendered on the mobile BCIs. Finally, this study performed an online test with the Tablet-based BCI system and obtained an averaged information transfer rate of 33.87 bits/min in three subjects. The current integration leads to a truly practical and ubiquitous SSVEP BCI on mobile devices for real-life applications.

Using robust principal component analysis to alleviate day-to-day variability in EEG based emotion classification

An emerging challenge for emotion classification using electroencephalography (EEG) is how to effectively alleviate day-to-day variability in raw data. This study employed the robust principal component analysis (RPCA) to address the problem with a posed hypothesis that background or emotion-irrelevant EEG perturbations lead to certain variability across days and somehow submerge emotion-related EEG dynamics. The empirical results of this study evidently validated our hypothesis and demonstrated the RPCA's feasibility through the analysis of a five-day dataset of 12 subjects. The RPCA allowed tackling the sparse emotion-relevant EEG dynamics from the accompanied background perturbations across days. Sequentially, leveraging the RPCA-purified EEG trials from more days appeared to improve the emotion-classification performance steadily, which was not found in the case using the raw EEG features. Therefore, incorporating the RPCA with existing emotion-aware machine-learning frameworks on a longitudinal dataset of each individual may shed light on the development of a robust affective brain-computer interface (ABCI) that can alleviate ecological inter-day variability.

Empirical mode decomposition improves detection of SSVEP

Steady State Visual Evoked Potentials (SSVEPs) have been used to quantify attention-related neural activity to visual targets. This study investigates how empirical mode decomposition (EMD) can improve detection accuracy and rate of SSVEPs. First, the scalp-recorded electroencephalogram (EEG) signals are decomposed into intrinsic mode functions (IMFs) by EMD. Then, IMF components accounting for SSVEPs are selected for target frequency detection. Finally, target frequency is identified by two methods: Gabor transform and Canonical Correlation Analysis (CCA). This study quantitatively explores the impact of EMD on the target frequency detection. Empirical results show that the EMD improves their recognition accuracy when Gabor transform is used, even in a shorter Gaussian window, but has little effects on the performance of the CCA. Further, this study finds that harmonic responses of the target frequency can be used to enhance the SSVEP detection both for the Gabor transform and CCA.

Explore the Functional Connectivity between Brain Regions during a Chemistry Working Memory Task

Previous studies have rarely examined how temporal dynamic patterns, event-related coherence, and phase-locking are related to each other. This study assessed reaction-time-sorted spectral perturbation and event-related spectral perturbation in order to examine the temporal dynamic patterns in the frontal midline (F), central parietal (CP), and occipital (O) regions during a chemistry working memory task at theta, alpha, and beta frequencies. Furthermore, the functional connectivity between F-CP, CP-O, and F-O were assessed by component event-related coherence (ERCoh) and component phase-locking (PL) at different frequency bands. In addition, this study examined whether the temporal dynamic patterns are consistent with the functional connectivity patterns across different frequencies and time courses. Component ERCoh/PL measured the interactions between different independent components decomposed from the scalp EEG, mixtures of time courses of activities arising from different brain, and artifactual sources. The results indicate that the O and CP regions’ temporal dynamic patterns are similar to each other. Furthermore, pronounced component ERCoh/PL patterns were found to exist between the O and CP regions across each stimulus and probe presentation, in both theta and alpha frequencies. The consistent theta component ERCoh/PL between the F and O regions was found at the first stimulus and after probe presentation. These findings demonstrate that temporal dynamic patterns at different regions are in accordance with the functional connectivity patterns. Such coordinated and robust EEG temporal dynamics and component ERCoh/PL patterns suggest that these brain regions’ neurons work together both to induce similar event-related spectral perturbation and to synchronize or desynchronize simultaneously in order to swiftly accomplish a particular goal. The possible mechanisms for such distinct component phase-locking and coherence patterns were also further discussed.

Neural Correlates of Mathematical Problem Solving

This study explores electroencephalography (EEG) brain dynamics associated with mathematical problem solving. EEG and solution latencies (SLs) were recorded as 11 neurologically healthy volunteers worked on intellectually challenging math puzzles that involved combining four single-digit numbers through basic arithmetic operators (addition, subtraction, division, multiplication) to create an arithmetic expression equaling 24. Estimates of EEG spectral power were computed in three frequency bands — θ (4–7 Hz), α (8–13 Hz) and β (14–30 Hz) — over a widely distributed montage of scalp electrode sites. The magnitude of power estimates was found to change in a linear fashion with SLs — that is, relative to a base of power spectrum, theta power increased with longer SLs, while alpha and beta power tended to decrease. Further, the topographic distribution of spectral fluctuations was characterized by more pronounced asymmetries along the left–right and anterior–posterior axes for solutions that involved a longer search phase. These findings reveal for the first time the topography and dynamics of EEG spectral activities important for sustained solution search during arithmetical problem solving.