Since the launch of the first consumer grade EEG measuring sensors ‘NeuroSky Mindset’ in 2007, the market has witnessed an introduction of at least one new product every year by competing manufacturers, which include NeuroSky, Emotiv, interaXon and OpenBCI. There are numerous variations in the make and versions, but these products clearly share the key selling points of affordability, portability, and ease of use. These features are patently well placed provided one of the main objectives for their development is to attract a new target group of commercial users. Nevertheless, with several decades of traditional EEG usage in clinical and experimental settings, the shift toward commercial and engineering sides has not been achieved without skepticism. With this in mind, researchers in related fields have been tirelessly working to ensure that these putatively novel features were not introduced at the expense of efficiency and accuracy by conducting validation studies to compare the performance of data derived from consumer grade EEG devices with ones from standard research grade counterparts. In this review, we seek to provide the detail of the products supplied by the major players, summarize studies that evaluate consumer product’s performance against research grade devices, the key areas of applications that these consumer grade devices have been employed in over the past five years or so, and finally give our perspectives on the limitations and what these innovative tools could offer going forward in terms of research and commercial applications.

Consumer Grade EEG Measuring Sensors as Research Tools: A Review

A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities.

Cognitive workload is one of the widely invoked human factors in the areas of human-machine interaction (HMI) and neuroergonomics The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 8931% average accuracy for brain-computer interface (BCI) The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant Real-time four-level MWL states are assessed using fNIRS system, and initial classification is performed using three strong machine learning (ML) techniques, support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural network (ANN) with obtained average accuracies of 5433, 5431, and 6936%, respectively In this study, novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 8745 and 8931% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem Statistical analysis, t-test, and one-way F-test (ANOVA) are also performed on accuracies obtained through ML and DL algorithms Results show that the proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared with ML (SVM, ANN, and k-NN) algorithms

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Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface.

Over the past 10 years, we (the Purdue Stuttering Project) have implemented longitudinal studies to examine factors related to persistence and recovery in early childhood stuttering. Stuttering develops essentially as an impairment in speech sensorimotor processes that is strongly influenced by dynamic interactions among motor, language, and emotional domains. Our work has assessed physiological, behavioral, and clinical features of stuttering within the motor, linguistic, and emotional domains. We describe the results of studies in which measures collected when the child was 4 to 5 years old are related to eventual stuttering status. We provide supplemental evidence of the role of known predictive factors (e.g., sex and family history of persistent stuttering). In addition, we present new evidence that early delays in basic speech motor processes (especially in boys), poor performance on a nonword repetition test, stuttering severity at the age of 4 to 5 years, and delayed or atypical functioning in central nervous system language processing networks are predictive of persistent stuttering.

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What Are Predictors for Persistence in Childhood Stuttering

Electroencephalogram (EEG) measurement, being an appropriate approach to understanding the underlying mechanisms of the major depressive disorder (MDD), is used to discriminate between depressive and normal control. With the advancement of deep learning methods, many studies have designed deep learning models to improve the classification accuracy of depression discrimination. However, few of them have focused on designing a convolutional filter to learn features according to EEG activity characteristics. In this study, a novel convolutional neural network named HybridEEGNet that is composed of two parallel lines is proposed to learn the synchronous and regional EEG features, and further differentiate normal controls from medicated and unmedicated MDD patients. A ten-fold cross validation method is used to train and test the model. The results show that HybridEEGNet achieves a sensitivity of 68.78%, a specificity of 84.45%, and an accuracy of 79.08% in three-category classification. The result of EEG feature analysis indicates that the differences of spatial distributions and amplitude ranges in the alpha rhythm (especially at approximately 10 Hz) among three categories might be distinctive attributes for depression discrimination.

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HybridEEGNet: A Convolutional Neural Network for EEG Feature Learning and Depression Discrimination

Assessment of text relevance is an important aspect of human–information interaction. For many search sessions it is essential to achieving the task goal. This work investigates text relevance decision dynamics in a question-answering task by direct measurement of eye movement using eye-tracking and brain activity using electroencephalography EEG. The EEG measurements are correlated with the user's goal-directed attention allocation revealed by their eye movements. In a within-subject lab experiment (N = 24), participants read short news stories of varied relevance. Eye movement and EEG features were calculated in three epochs of reading each news story (early, middle, final) and for periods where relevant words were read. Perceived relevance classification models were learned for each epoch. The results show reading epochs where relevant words were processed could be distinguished from other epochs. The classification models show increasing divergence in processing relevant vs. irrelevant documents after the initial epoch. This suggests differences in cognitive processes used to assess texts of varied relevance levels and provides evidence for the potential to detect these differences in information search sessions using eye tracking and EEG.

Temporal dynamics of eye-tracking and EEG during reading and relevance decisions

Stuttering is a communication disorder that affects approximately 1% of the population. Although 5–8% of preschool children begin to stutter, the majority will recover with or without intervention. There is a significant gap, however, in our understanding of why many children recover from stuttering while others persist and stutter throughout their lives. Detecting neurophysiological biomarkers of stuttering persistence is a critical objective of this paper. In this paper, we developed a novel supervised sparse feature learning approach to discover discriminative biomarkers from functional near infrared spectroscopy (fNIRS) brain imaging data recorded during a speech production experiment from 46 children in three groups: children who stutter ( ${n}=\textsf {16}$ ); children who do not stutter ( ${n}=\textsf {16}$ ); and children who recovered from stuttering ( ${n}=\textsf {14}$ ). We made an extensive feature analysis of the cerebral hemodynamics from fNIRS signals and selected a small number of important discriminative features using the proposed sparse feature learning framework. The selected features are capable of differentiating neural activation patterns between children who do and do not stutter with an accuracy of 87.5% based on a five-fold cross-validation procedure. The discovered set cerebral hemodynamics features are presented as a set of promising biomarkers to elucidate the underlying neurophysiology in children who have recovered or persisted in stuttering and to facilitate future data-driven diagnostics in these children.

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An fNIRS-Based Feature Learning and Classification Framework to Distinguish Hemodynamic Patterns in Children Who Stutter

EEG source imaging integrates temporal and spatial components of EEG to localize the generating source of electrical potentials based on recorded EEG data on the scalp. As EEG sensors can't directly measure activated brain sources, many approaches were proposed to estimate brain source activation pattern given EEG data. However, since most part of the brain activity is composed of the spontaneous non-task related activations, true task caused activation sources will be corrupted in strong background signal. For decades, the EEG inverse problem was solved in an unsupervised way without any utilization of the label information that represents different brain states. We propose that by leveraging label information, the task related discriminative sources can be much better retrieved among strong spontaneous background signals. A novel model for solving EEG inverse problem called Laplacian Graph Regularized Discriminative Source Reconstruction which aims to explicitly extract the discriminative sources by implicitly coding the label information into the graph regularization term. The proposed model can be generally extended with different assumptions. The extension of our framework is applied to VB-SCCD model which aim to estimate extended brain sources by including a spatial total variation regularization term. Simulated results show the effectiveness of the proposed framework.

Graph Regularized EEG Source Imaging with In-Class Consistency and Out-Class Discrimination

As Electroencephalography (EEG) is a non-invasive brain imaging technique that records the electric field on the scalp instead of direct measuring activities of brain voxels on the cortex, many approaches were proposed to estimate the activated sources due to its significance in neuroscience research and clinical applications. However, since most part of the brain activity is composed of the spontaneous neural activities or non-task related activations, true task relevant activation sources can be very challenging to be discovered given strong background signals. For decades, the EEG source imaging problem was solved in an unsupervised way without taking into consideration the label information that representing different brain states (e.g. happiness, sadness, and surprise). A novel model for solving EEG inverse problem called Graph Regularized Discriminative Source Imaging (GRDSI) was proposed, which aims to explicitly extract the discriminative sources by implicitly coding the label information into the graph regularization term. The proposed model is capable of estimating the discriminative brain sources under different brain states and encouraging intra-class consistency. Simulation results show the effectiveness of our proposed framework in retrieving the discriminative sources.

Supervised Discriminative EEG Brain Source Imaging with Graph Regularization

Recommended Citation Hosseini, Rahilsadat. "Wastewater's total influent estimation and performance modeling: a data driven approach.

https://ir.uiowa.edu/cgi/viewcontent.cgi?article=2696&context=etd

Rahilsadat Hosseini

Papers

Temporal dynamics of eye-tracking and EEG during reading and relevance decisions

An fNIRS-Based Feature Learning and Classification Framework to Distinguish Hemodynamic Patterns in Children Who Stutter

Graph Regularized EEG Source Imaging with In-Class Consistency and Out-Class Discrimination

Supervised Discriminative EEG Brain Source Imaging with Graph Regularization

Wastewater's total influent estimation and performance modeling: a data driven approach