Rahul Gavas

A dynamic attention assessment and enhancement tool using computer graphics

Training Programs to enhance Math Solving Skills, Memory, Visualization, etc in children are gaining popularity worldwide. Any skill is better acquired, when attention, the basic cognitive ability of the trainee is improved. This study makes an attempt to devise a technique in the form of a Brain Computer Interface (BCI) Game, to assist the trainers in monitoring and evaluating the attention levels of the trainees, at regular intervals during the training period.The gaming environment is designed using Open Source Graphics Library (OpenGL) package and the game control is through the player’s brain waves using the BCI technology. The players control the movement of an object from a source to a destination location on the screen by focussing their thought processes. The time taken to complete one game can be recorded. More the time taken, lesser would be the attention sustaining capacity of the player.Thirteen subjects under different levels of the ABACUS Math Solving training program controlled the ball movement while solving math problems mentally, the time taken reduced for most of the subjects as they reached higher levels of their training course, indicating the benefit of such training programmes. The game was also played by eight non-abacus literates. The evaluation procedure was found to be very easy and fast.

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 %.

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.

Enhancing the usability of low-cost eye trackers for rehabilitation applications

Eye tracking is one of the most widely used technique for assessment, screening and human-machine interaction related applications. There are certain issues which limit the usage of eye trackers in practical scenarios, viz., i) need to perform multiple calibrations and ii) presence of inherent noise in the recorded data. To address these issues, we have proposed a protocol for one-time calibration against the “regular” or the “multiple” calibration phases. It is seen that though it is always desirable to perform multiple calibration, the one-time calibration also produces comparable results and might be better for individuals who are not able to perform multiple calibrations. In that case, “One-time calibration” can also be done by a participant and the calibration results are used for the rest of the participants, provided the chin rest and the eye tracker positions are unaltered. The second major issue is the presence of the inherent noise in the raw gaze data, leading to systematic and variable errors. We have proposed a signal processing chain to remove these two types of errors. Two different psychological stimuli-based tasks, namely, recall-recognition test and number gazing task are used as a case study for the same. It is seen that the proposed approach gives satisfactory results even with one-time calibration. The study is also extended to test the effect of long duration task on the performance of the proposed algorithm and the results confirm that the proposed methods work well in such scenarios too.

Cognitive load and metacognitive confidence extraction from pupillary response

Abstract Spontaneous pupillary fluctuations are indicative of the cognitive load imposed while doing a task involving memory resources. However, the fluctuations are also dependent on other factors like lighting conditions, uncertainty or the level of confidence while performing the task and so on. This paper aims to separate various components of pupillary response in order to assess the cognitive load and the confidence with which the task is performed. Hybrid decomposition models using ensemble empirical mode decomposition followed by independent component analysis is found to effectively reconstruct the original signal. The variational Mode Decomposition has been used in order to overcome the limitations imposed by empirical mode decomposition. Results show that variational mode decomposition outperforms existing state-of-the-art methods. Further, we attempted to identify the hidden components of pupillary response during cognitive tasks like mental addition and the anagram test. We obtained F score of 0.67 in the detection of cognitive load and F score of 0.99 for the detection of confidence level from the single channel pupil data.