Debatri Chatterjee

EEG-based fuzzy cognitive load classification during logical analysis of program segments

The paper aims at designing a novel scheme for cognitive load classification of subjects engaged in program analysis. The logic of propositions has been employed here to construct program segments to be used for cognitive load analysis and classification. Electroencephalogram signals acquired from the subjects during program analysis are first fuzzified and the resultant fuzzy membership functions are then submitted to the input of a fuzzy rule-based classifier to determine the class of the cognitive load of the subjects. Experimental results envisage that the proposed classifier has a good classification accuracy of 86.2%. Performance analysis of the fuzzy classifier further reveals that it outperforms two most widely used classifiers: Support Vector Machine and Naive Bayes classifier.

Evaluation of Different Onscreen Keyboard Layouts Using EEG Signals

The paper aims at evaluation of different onscreen keyboard layouts based on the biological responses of the users. The signal used for the said purpose is Electroencephalogram acquired by low cost neuro-headset from Emotiv. We propose to use human cognition as the fundamental feature to discriminate between user-friendly vs. cumbersome onscreen layout designs. To validate our observations we compared our results with bench marked data based on user study and KLM-GOMS model. A classifier is first trained for high and low cognition tasks based on well-established cognitive tests (e.g. Stroop test) and then this classifier is used to report the cognition class for a particular onscreen layout. A high cognition load class indicates complexity in the layout design whereas a low cognition output indicates the layout to be user friendly. Present evaluation methods like user study or KLM-GOMS based model, serves as an indirect measure of goodness of layout designs. In contrast, our approach has a unique advantage as this is a direct measure of humans biological response subjected to stimuli (in our case onscreen keyboard layouts) hence more reliable.

Cognitive load measurement - A methodology to compare low cost commercial EEG devices

Use of EEG signals in measuring cognitive load is a widely practiced area and falls under Brain-Computer-Interfacing (BCI) technology. However this technology uses medical grade EEG devices that are expensive as well as not user-friendly for regular use. Recent launch of low cost wireless EEG headsets from different companies opens up the possibility for commercialization of BCI and thus drew attention of the research community all over the world. While there are numerous studies on BCI with the use of medical grade devices there are limited numbers of papers reported on those using low cost devices. Moreover, reports on evaluating relative performance of these commercially available EEG devices based on a specific BCI experiment are minuscule. This paper attempts to fill this gap and presents a methodology to compare with various aspects between two widely used low cost wireless EEG devices namely Emotiv and Neurosky for application in cognitive load detection.

Unsupervised approach for measurement of cognitive load using EEG signals

Individuals exhibit different levels of cognitive load for a given mental task. Measurement of cognitive load can enable real-time personalized content generation for distant learning, usability testing of applications on mobile devices and other areas related to human interactions. Electroencephalogram (EEG) signals can be used to analyze the brain-signals and measure the cognitive load. We have used a low cost and commercially available neuro-headset as the EEG device. A universal model, generated by supervised learning algorithms, for different levels of cognitive load cannot work for all individuals due to the issue of normalization. In this paper, we propose an unsupervised approach for measuring the level of cognitive load on an individual for a given stimulus. Results indicate that the unsupervised approach is comparable and sometimes better than supervised (e.g. support vector machine) method. Further, in the unsupervised domain, the Component based Fuzzy c-Means (CFCM) outperforms the traditional Fuzzy c-Means (FCM) in terms of the measurement accuracy of the cognitive load.

Inactive-state recognition from EEG signals and its application in cognitive load computation

Extraction of desirable information from electroencephalogram signals require same level of active involvement from the participants throughout the entire duration of the task. However, this is hard to attain due to environmental, personal and internal factors including thought processes. This poses a major challenge in realizing accurate evaluation of mental workload. This study is aimed at detection of the inactive mental states of the participant during an experimental task. Conventionally cognitive load is computed with respect to the baseline period. Here a novel approach is adopted based on the detection of most inactive mental state during the rest period. It is observed that alpha rhythms (8 – 12 Hz) are dominant than theta rhythms (4 – 7 Hz) during the rest state and this information is used in determining the most inactive mental states. Galvanic skin response (GSR) is also analyzed for the same purpose to validate the decoded mental state from the brain signals. Results indicate that the proposed approach of inactivity detection, improves the overall accuracy of detection of cognitive load by 15.57 %.

Analyzing elementary cognitive tasks with Bloom's taxonomy using low cost commercial EEG device

Cognitive load primarily depends on how an individual perceives, assimilates and responds to an external stimulus. We intend to create Electroencephalogram (EEG) models for the cognitive skills defined in the Blooms taxonomy using low cost, commercial EEG devices. This could be applied in educational psychology to provide individual assistance according to ones learning style and abilities. The major challenge in using low resolution EEG device lies in signal analysis with reduced number of channels. In this paper, we present the signature of EEG signals for such low cost devices using three basic tasks namely, number matching; finding characters in text; finding hidden patterns and figures. These tasks map with understand, remember and analyze sub categories of Blooms taxonomy. Different brain regions are activated while performing the above tasks. However, the EEG signals observed on the scalp are the manifestation of the combined effects of various brain regions. The cleaned EEG signals are analyzed using unsupervised clustering of features obtained from different frequency bands. A study is performed on 10 subjects using 14 lead Emotiv neuroheadset, so that one can get further insights on how an individual perceives certain cognitive tasks.

Constrained Kalman filter for improving Kinect based measurements

Microsoft Kinect has the huge potential to be used in home-based rehabilitation and clinical assessments for patients suffering from stroke or other neurological disorder, due to its affordability and unobtrusiveness in analysing joint kinematics. However, skeleton data obtained from Kinect Xbox 360 (Kinect 1) or Kinect Xbox One (Kinect 2) are usually noisy which affects accuracy of estimation of three dimensional joint locations. The noise profile varies for both stationary and dynamic postures and it affects anthropometric measurements of the body segments connecting any two joints. We propose a novel approach to constrain a standard Kalman filter, based on the dynamics of individual joints, in order to keep the distance between any two physically connected joints (namely bone length) constant over time. Our constrained Kalman filter method not only tracks the joints accurately but also reduces the variation in bone lengths by 92% and 94% for Kinect 2 and 1 respectively.

Analysis of Cognitive Load -- Importance of EEG Channel Selection for Low Resolution Commercial EEG Devices

Measurement of cognitive load using brain signalsis an important area of research in human behavior and psychology. Recently, there have been attempts to use low cost, commercially available Electroencephalogram (EEG) devices for the analysis of the cognitive load. Due to the reduced number of leads, these low resolution devices pose major challenges in signal processing as well as in feature extraction. In this paper, we investigate the significant leads or channels that are useful for the analysis of the cognitive load. We use a standard matching test and n-back memory test imparting low and high cognitive loads respectively. The investigation is based on the analysis of variance (ANOVA) of Alpha and Theta frequency band signals for various combinations of leads. Comparisons have been done between the previously reported leads and those obtained using a few feature selection algorithms. Results indicate that for a given stimulus, though the significant leads are very much dependent on the subjects, the leads corresponding to the left frontal lobe and right parieto-occipital lobe are in general most significant across majority of subjects for analysis of the cognitive load.

Artifact Removal from EEG Signals Recorded Using Low Resolution Emotiv Device

Electroencephalogram (EEG) signals are of very low amplitude and are easily contaminated by different types of noises like environmental and of non-cerebral in nature. Thus signal pre-processing is a major challenge while dealing with applications involving EEG signals. The scenario becomes much more complex while using commercially available, low resolution devices as they have fewer electrodes. In this paper, we have applied some of the widely used signal processing techniques to get rid of eye blink and noise related artifacts from EEG signals recorded using a low cost wireless device from Emotiv. Investigations reveal that clustering based eye blink detection method and the skewness based noise detection method give the best detection accuracy. As an example use-case, we show how selective filtering of the EEG signals in the blink regions and removal of noisy windows can help in improving the discrimination power between the two types of color Stroop stimulus based cognitive load analysis. Thus with appropriate signal pre-processing techniques, these low resolution devices can be successfully used to differentiate between different levels of mental workload, which in turn makes these devices useful for non-medical Brain Computer Interface (BCI) applications requiring mass deployment.

Validation of stimulus for EEG signal based cognitive load analysis

Cognitive load is defined as the mental workload imparted on brain while doing a task. The amount of cognitive load experienced depends on individuals ability of perception, assimilation and response to a task. Real-time measurement of the level of cognitive load using low cost Electroencephalogram (EEG) signal enables understanding of personal cognitive skills. In this paper, we propose a methodology of selecting a reference task whose bio-markers closely match with a given task while probing different cognitive abilities. The benefit of this approach is to have a limited set of training models for the reference tasks related to various cognitive categories and use the same for a variety of unknown tasks. Experiment is performed for two levels of cognitive load with three different tasks namely Stroop color task, logical reasoning task and usage of on-screen keyboards. The training models of the reference tasks, selected by cluster analysis of low and high cognitive levels are used to evaluate an unknown task. Experimental results indicate that the Stroop is a better reference for On-Screen keyboard test compared to the Logical reasoning test. Support vector machine (SVM) and principal component analysis (PCA) followed by SVM (PCA-SVM) are used as the classifiers for the testing.