Abstract: The present disclosure envisages a computer implemented system and method to derive a relationship between Elementary Cognitive Tasks (ECTs) and the underlying cognitive skills of individuals through Electroencephalogram (EEG) analysis. The aim is to evaluate or improve the perceptual-cognitive traits of a subject that comprises disintegrating a given task into elementary task that are further mapped to identified cognitive categories of Bloom's Taxonomy, upon which a cluster analysis is performed. The separation index between the clusters thereafter establishes that individuals have different thinking process which is characteristics of that subject.

Abstract: A method and system is provided for assessing the learning experience of the person by monitoring the mental state of the person. The method involves measuring the brain signal, skin conductance using GSR device, and heart rate variability using the pulse oximeter. These physiological signals are measured when the person is performing an activity such as the modified Stroop test. Once the activity is performed, an offline questionnaire is also filled by the person. Based on the comparison of the offline questionnaire and the physiological signals, a model is generated. This model is used to assess the learning experience of the person. According to another embodiment, a method is also provided for maintaining the steady flow state of a person while performing any activity.

Abstract: System and method for evaluating a cognitive load on a user, corresponding to a stimulus is disclosed. Electroencephalogram (EEG) data corresponding to the stimulus of a user is received. The stimulus corresponds to a mental task performed by the user. The EEG data is split into a plurality of slots. A slot of the plurality of slots comprises a subset of the EEG data. One or more EEG features are extracted from the subset of the EEG data. The one or more EEG features are represented in one of a frequency domain and a time domain. A plurality of data points present in the one or more EEG features is grouped into two or more clusters using an unsupervised learning technique. The two or more clusters comprise one or more data points of the plurality of data points. The one or more data points correspond to a level of the cognitive load.

Abstract: Disclosed are methods and systems for evaluating onscreen keyboards. The method comprises receiving a first set of parameters and a second set of parameter associated with a first onscreen keyboard and a second onscreen keyboard, respectively. The method further comprises determining a first cognitive score for the first onscreen keyboard using the first set of parameters. The method further comprises determining a second cognitive score for the second onscreen keyboard using the second set of parameters. The method further comprises validating the first cognitive score and the second cognitive score using an Electroencephalography (EEG) signal of the user. The EEG signal of the user is captured while the user is using the first onscreen keyboard and the second onscreen keyboard.

Abstract: Disclosed is a method and system for selection of Electroencephalography (EEG) channels valid for determining cognitive load of subject. According to one embodiment, EEG signals are obtained from EEG channels associated with subject performing cognitive tasks are received. Time-frequency features of EEG signals are extracted for a frequency band comprise maximum energy value, minimum energy value, average energy value, maximum frequency value, minimum frequency value, and average frequency value. Weight of an EEG channel associated with time-frequency feature is derived using statistical learning technique. Binary values for EEG channels corresponding to time-frequency feature are assigned using weight of EEG channel associated with time-frequency feature.

Abstract: Disclosed is a method and system for determining a cognitive load of a subject from Electroencephalography (EEG) signals. EEG signals are received from EEG channels associated with a left-frontal brain lobe. EEG signals are associated with a subject performing cognitive task. EEG signals are received from a low resolution EEG device. EEG channels comprise four EEG channels associated with the left-frontal brain lobe. EEG signals are preprocessed using a Hilbert-Huang Transform (HHT) filter to remove a noise corresponding to one or more non-cerebral artifacts to generate preprocessed EEG signals. Features comprising Fast Fourier Transform (FFT) based alpha and theta band power are extracted from the preprocessed EEG signals. Feature vector is generated from the features. The feature vector is classified using a Support Vector Machine (SVM) classifier to determine the cognitive load of the subject.

Abstract: Disclosed is a system and method for personalizing an appliance's functioning remotely in real-time. The user profile and set of parameters affecting the functionality of the appliance are received by a computing device by, for example, communication module and are matched by a data delivery device to pre-defined parameters stored therein, for the profile categorization. An output profile is generated and is defined by matched parameters. The appliance is remotely controlled by modifying the functioning of the appliance in accordance with the output profile defined by the matched parameters.

Abstract: Disclosed are methods and systems for evaluating onscreen keyboards. The method comprises receiving a first set of parameters and a second set of parameter associated with a first onscreen keyboard and a second onscreen keyboard, respectively. The method further comprises determining a first cognitive score for the first onscreen keyboard using the first set of parameters. The method further comprises determining a second cognitive score for the second onscreen keyboard using the second set of parameters. The method further comprises validating the first cognitive score and the second cognitive score using an Electroencephalography (EEG) signal of the user. The EEG signal of the user is captured while the user is using the first onscreen keyboard and the second onscreen keyboard.