Lenis Mauricio Merino

Characterization and Robust Classification of EEG Signal from Image RSVP Events with Independent Time-Frequency Features

This paper considers the problem of automatic characterization and detection of target images in a rapid serial visual presentation (RSVP) task based on EEG data. A novel method that aims to identify single-trial event-related potentials (ERPs) in time-frequency is proposed, and a robust classifier with feature clustering is developed to better utilize the correlated ERP features. The method is applied to EEG recordings of a RSVP experiment with multiple sessions and subjects. The results show that the target image events are mainly characterized by 3 distinct patterns in the time-frequency domain, i.e., a theta band (4.3 Hz) power boosting 300–700 ms after the target image onset, an alpha band (12 Hz) power boosting 500–1000 ms after the stimulus onset, and a delta band (2 Hz) power boosting after 500 ms. The most discriminant time-frequency features are power boosting and are relatively consistent among multiple sessions and subjects. Since the original discriminant time-frequency features are highly correlated, we constructed the uncorrelated features using hierarchical clustering for better classification of target and non-target images. With feature clustering, performance (area under ROC) improved from 0.85 to 0.89 on within-session tests, and from 0.76 to 0.84 on cross-subject tests. The constructed uncorrelated features were more robust than the original discriminant features and corresponded to a number of local regions on the time-frequency plane. Availability: The data and code are available at: http://compgenomics.cbi.utsa.edu/rsvp/index.html

A Deep Learning method for classification of images RSVP events with EEG data

In this paper, we investigated Deep Learning (DL) for characterizing and detecting target images in an image rapid serial visual presentation (RSVP) task based on EEG data. We exploited DL technique with input feature clusters to handle high dimensional features related to time - frequency events. The method was applied to EEG recordings of a RSVP experiment with multiple sessions and subjects. For classification of target and non-target images, a deep belief net (DBN) classifier was based on the uncorrelated features, which was constructed from original correlated features using clustering method. The performance of the proposed DBN was tested for different combinations of hidden units and hidden layers on multiple subjects. The results of DBN were compared with cluster Linear Discriminant Analysis (cLDA) and Support vector machine (SVM) and DBN demonstrated better performance in all tested cases. There was an improvement of 10 - 25% for certain cases. We also demonstrated how DBN is used to characterize brain activities.

Classification of non-time-locked rapid serial visual presentation events for brain-computer interaction using deep learning

Deep learning solutions based on deep neural networks (DNN) and deep stack networks (DSN) were investigated for classifying target images in a non-time-locked rapid serial visual presentation (RSVP) image target identification task using EEG. Several feature extraction methods associated with this task were implemented and tested for deep learning, where a sliding window method using the trained classifier was used to predict the occurrence of target events in a non-time-locked fashion.. The deep learning algorithms explored based on deep stacking networks were able to improve the error rate by about 5% over existing algorithms such as linear discriminant analysis (LDA) for this task. Initial test results also showed that this method based on deep stacking networks for non-time-locked classification can produce an error rate close to that achieved for time-locked classification, thus illustrating the power of deep learning for complex feature spaces.

A bag-of-words model for task-load prediction from EEG in complex environments

Neurotechnologies based on electroencephalography (EEG) and other physiological measures to improve task performance in complex environments will require tools and analysis methods that can account for increased environmental noise and task complexity compared to traditional neuroscience laboratory experiments. We propose a bag-of-words (BoW) model to address the difficulties associated with realistic applications in complex environments. In this paper, our proof-of-concept results show that a BoW classifier can discriminate two task-relevant states (high versus low task-load) while an individual performs a simulated security patrol mission with complex, concurrent tasking. Classifier performance is largely consistent across six simulation missions for a given participant, but performance decreases when trying to predict between two individuals. Overall, these initial results suggest that this BoW approach holds promise for detecting task-relevant states in real-world settings.

Asynchronous control of unmanned aerial vehicles using a steady-state visual evoked potential-based brain computer interface

AbstractThe goal of this study is to design an asynchronous steady-state visual evoked potential (SSVEP) BCI system to enable control of an unmanned aerial vehicle (UAV) with multiple commands. An SSVEP-based BCI system with six different flickering frequencies was constructed to realize six actuation commands for UAV control. In addition, asynchronous control was achieved by including a detection of the ‘idle’ brain state using a novel likelihood ratio test and the hover command was implemented for the idle state. Offline recording was conducted to evaluate the detection accuracies and a game-like online experiment was also conducted to assess the online performance of the proposed system. Forty-two subjects participated in offline recordings to evaluate the detection accuracy of commands as well as detection of the ‘idle’ state. An average error rate of 15% was obtained for detecting the six commands, whereas an average error rate of 23.06% was obtained for differentiating commands from idle brain state...

Classification of imperfectly time-locked image RSVP events with EEG device

Classification based on EEG data in an RSVP experiment is considered. Although the latency in neural response relative to the stimulus onset time may be more realistically considered to vary across trials due to factors such as subject fatigue and environmental distractions, it is nevertheless assumed to be time-locked to the stimulus in most of the existing work as a means to alleviate the computational complexity. We consider here a more practical scenario that allows variation in response latency and develop a rigorous statistical formulation for modeling the uncertainty within the varying latency coupled with a likelihood ratio test (LRT) for classification. The new model not only improves the EEG classification performance, but also may predict the true stimulus onset time when this information is not precisely available. We test the proposed LRT algorithm on an EEG data set from an image RSVP experiment and show that, by admitting the latency variation, the proposed approach consistently outperforms a method that relies on perfect time-locking (AUC: 0.88 vs 0.86), especially when the stimulus onset time is not precisely available (AUC: 0.84 vs 0.71). Furthermore, the predicted stimulus onset times are highly enriched around the true onset time with p-value =5.2×10−44

Detection of control or idle state with a likelihood ratio test in asynchronous SSVEP-based brain-computer interface systems

= 5.2 \times 10^{-44}

Exploiting correlated discriminant features in time frequency and space for characterization and robust classification of image RSVP events with EEG data

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Classification of EEG recordings without perfectly time-locked events

We consider the detection of the control or idle state in an asynchronous Steady-state visually evoked potential (SSVEP)-based brain computer interface system. We propose a likelihood ratio test using Canonical Correlation Analysis (CCA) scores calculated from the EEG measurements. The test exploits the state-specific distributions of CCA scores. The algorithm was tested on offline measurements from 42 participants and the results should a significant improvement in detection error rate over the support vector machine classifier. The proposed test is also shown to be robust against training sample size.

Classification of Imperfectly Time-Locked Image RSVP Events with EEG Device

In this paper, the problem of automatic characterizing and detecting target images in an image rapid serial visual presentation (RSVP) task based on EEG data is considered. A novel method that aims at identifying event-related potentials (ERPs) in time-frequency is proposed, and a robust classifier with feature clustering is developed to better utilize the correlated ERP features. The method is applied to EEG recordings of a RSVP experiment with multiple sessions and subjects. The results show that, the target image events are mainly characterized by 3 distinct patterns in the time-frequency domain, i.e., a theta band (4.3 Hz) power boost 300-700 ms after the target image onset, an alpha band (12 Hz) power repression 500-1000 ms after the stimulus onset, and a delta band (2 Hz) power boost after 500 ms. The discriminate time-frequency features are mostly power boost and relatively consistent among multiple sessions and subjects. These features are visualized for later analysis. For classification of target and non-target images, our LDA classifier was based on the uncorrelated features, which was constructed from original correlated features using clustering method. With feature clustering, the performance (area under ROC) was improved from 0.85 to 0.89 for within-session tests, and from 0.76 to 0.84 for cross-subject tests. Meanwhile, the constructed uncorrelated features were shown more robust than the original discriminant features, and corresponding to a local region in time-frequency.