AU2021102774A4

AU2021102774A4 - A system and a method for assessing mental ability of a user using an electroencephalogram

Info

Publication number: AU2021102774A4
Application number: AU2021102774A
Authority: AU
Inventors: Annushree Bablani; Dharavath Ramesh; Damodar Reddy Edla; P. Saidi Reddy; Diwakar Tripathi
Original assignee: Bablani Annushree Ms
Current assignee: Bablani Annushree Ms
Priority date: 2021-05-23
Filing date: 2021-05-23
Publication date: 2022-03-31
Anticipated expiration: 2029-05-23

Abstract

The present disclosure relates to a system and a method for assessing mental ability of a user using an electroencephalogram. A method and a system for assessing the subject's mental capacity or ability by analysing the cognitive responses obtained from the subject by measuring the biological brain activity; Obtaining the responses by providing subjects with stimuli related to the mental task presented to the subject. The presentation of stimuli is in the form of images, words and numbers. The presentation of stimuli to the subject through a computer program; the image stimuli presented will provide the visual memory and selective attention; word stimuli (synonym and antonym) will provide learning ability and aptitude; number stimuli will provide short term memory and promptness in the response. Utilizing the acquired data during the presentation of stimuli for classifying the subject's mental ability. Classification of data into low, average and high mental capacity using Spiking Neural Network; Determining the potentials generated by the subject using ERP plots. ERP plot generated after the presentation of each stimulus. It provides the average of the potential generated after stimuli. 14 r(4 00 0 0 0 0M cN cN( ( 00 3Q2 - >~ w~b E- 'U' 0 j 0 0) E = c2 2 c-2 ;. ts. '-'*C 0aE _ Z 0 Un4 E .2 tO to .2 tx ui0 m c- Gi 2 t2 wnu w L 0 Um-0 toY Eca Q' 0E LIJ 4 U Wa) [20 02 0x 0uQ - L > 0(N 0-o-CC

Description

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A SYSTEM AND A METHOD FOR ASSESSING MENTAL ABILITY OF A USER USING AN ELECTROENCEPHALOGRAM FIELD OF THE INVENTION

The present disclosure relates to a system and a method for assessing mental ability of a user using an electroencephalogram. It further utilizes the concept of the brain-computer interface by using EEG signals for the assessment of mental capacity.

BACKGROUND OF THE INVENTION

Since the beginning, the human race has always competed finding that who is more intelligent than whom. Many tests have been developed in past to analyze the IQ level of humans. Each person has a different level of intelligence. According to psychological science, intelligence is not only that is gained from a book but also by learning from experience. Intelligence is the ability to understand, learn and adapt consistently from the environment. Intelligence is "The aggregate or global capacity of the individual to act purposefully, to think rationally, and to deal effectively with his environment". Identifying the change in cognitive function of a person helps in tracking neurophysiological disorders, psychiatric disorders, Alzheimer disease etc. and in rehabilitation. As the cognitive function assessment helps diagnose mental health, it has also been utilized to diagnose the mental capacity of an individual. The main concern is lack of appropriate, efficient and inclusive tests which can effectively analyse an individual's mental capacity. There are few tests available online available which assess the mental ability of a person such as Wechsler memory scale, MemTrax test. But all these methods or tests measure indirect cognitive function whereas EEG, MEG, and fMRI etc. give a direct measure of cognitive activity. There are limitations for the direct measures too such as the psychological state of the person, tiredness, illness, or any other disorder which may compromise the accuracy of brain data recorded. To care for these limitations, there is a need to adequately controls these factors by not allowing such individuals for data recording, or should not perform recoding for a long time which may make the individual tired. These factors can be taken into consideration at the experimenter level. Among EEG, MEG and fMRI, EEG is easy to use and does not need rigorous training. Whereas MEG and fMRI equipment are huge and costly, also require extensive training and expertise to use. In this type of acquisition approaches where a direct view is recorded, the data depends on the subject's alertness. fMRI has been used to diagnose the behaviour of the subject by measuring the activity of the region of interest in US20090318794A1. The subject is said to perform some tasks which will selectively activate some regions of interest which are related to the given condition. EEG has been used for measuring mental function or emotional changes in US6434419B1, diagnosing brain capability before and after treatment in US20050273017A1, assessing an emotional response in US9558499B2, measuring a subject's working memory in US6434419B1, developing a system for eliciting and assessing an emotional response in CA2809827A1. In order to overcome the above-mentioned drawbacks, there is a need to develop a system and a method for assessing mental ability of a user using an electroencephalogram.

SUMMARY OF THE INVENTION

The present disclosure relates to a system and a method for assessing mental ability of a user using an electroencephalogram. A method and a system for assessing the subject's mental capacity or ability by analysing the cognitive responses obtained from the subject by measuring the biological brain activity; Obtaining the responses by providing subjects with stimuli related to the mental task presented to the subject. The presentation of stimuli is in the form of images, words and numbers. The presentation of stimuli to the subject through a computer program; the image stimuli presented will provide the visual memory and selective attention; word stimuli (synonym and antonym) will provide learning ability and aptitude; number stimuli will provide short term memory and promptness in the response. Utilizing the acquired data during the presentation of stimuli for classifying the subject's mental ability. Classification of data into low, average and high mental capacity using Spiking Neural Network; Determining the potentials generated by the subject using ERP plots. ERP plot generated after the presentation of each stimulus. It provides the average of the potential generated after stimuli.

In an embodiment, a system for assessing mental ability of a user using an electroencephalogram, comprises of: an acquisition module 102 comprising of a wearable electroencephalogram (EEG) module for extracting neurological signals from a brain of the user, wherein the wearable EEG module comprises of a plurality of electrodes positioned on a surface of the wearable EEG module and touching a scalp of the user for extracting the neurological signals from the brain; a signal processing module 104 connected to the acquisition module for processing the neurological signal, wherein the processing module comprises of: a feature extraction module 106 for recording event related potentials based on the neurological signals extracted from the brain during a particular task, wherein an amplitude and latency of event related potential is extracted from the neurological signals; and a classification module 108 connected to the feature extraction module for classifying the mental ability of the user, wherein the classification module uses a spiking neural network having a 3-fold classification technique.

In an embodiment, a method 200 for assessing mental ability of a user using an electroencephalogram comprises of the following steps: at step 202, extracting neurological signals from a brain of the user using an acquisition module comprising of a wearable electroencephalogram (EEG) module, wherein the wearable EEG module comprises of a plurality of electrodes positioned on a surface of the wearable EEG module and touching a scalp of the user for extracting the neurological signals from the brain; at step 204, processing the neurological signal using a signal processing module connected to the acquisition module, wherein the processing of neurological signal comprises of the following steps: at step 206, recording event related potentials using a feature extraction module based on the neurological signals extracted from the brain during a particular task, wherein an amplitude and latency of event related potential is extracted from the neurological signals; and at step 208, classifying the mental ability of the user using a classification module connected to the feature extraction module, wherein the classification module uses a spiking neural network having a 3-fold classification technique.

To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.

BRIEF DESCRIPTION OF FIGURES

These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

Figure 1, illustrates a system for assessing mental ability of a user using an electroencephalogram in accordance with an embodiment of the present disclosure.

Figure 2, illustrates a method for assessing mental ability of a user using an electroencephalogram in accordance with an embodiment of the present disclosure.

Figure 3, illustrates (a) The basic representation of the system involved in the execution of the method; (b) The modules involved in the method; (c) The structure of the presentation of stimuli to the subject; and (d) The classification results using SNN in accordance with an embodiment of the present disclosure.

Figure 4, illustrates (a) The ERP plot after the presentation of stimuli 1 for a subject; (b) The ERP plot after the presentation of stimuli 2 for a subject; (c) The ERP plot after the presentation of stimuli 3 for a subject; and (d) The ERP plot after the presentation of stimuli 4 for a subject in accordance with an embodiment of the present disclosure.

Figure 5, illustrates the filtered EEG data and original EEG data of a subject for few time samples in accordance with an embodiment of the present disclosure.

Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have been necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.

Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.

Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.

Referring to Figure 1, illustrates a system for assessing mental ability of a user using an electroencephalogram in accordance with an embodiment of the present disclosure. A system for assessing mental ability of a user using an electroencephalogram, comprises of: an acquisition module 102 comprising of a wearable electroencephalogram (EEG) module for extracting neurological signals from a brain of the user, wherein the wearable EEG module comprises of a plurality of electrodes positioned on a surface of the wearable EEG module and touching a scalp of the user for extracting the neurological signals from the brain; a signal processing module 104 connected to the acquisition module for processing the neurological signal, wherein the processing module comprises of: a feature extraction module 106 for recording event related potentials based on the neurological signals extracted from the brain during a particular task, wherein an amplitude and latency of event related potential is extracted from the neurological signals; and a classification module 108 connected to the feature extraction module for classifying the mental ability of the user, wherein the classification module uses a spiking neural network having a 3-fold classification technique.

Figure 2, illustrates a method for assessing mental ability of a user using an electroencephalogram in accordance with an embodiment of the present disclosure. A method 200 for assessing mental ability of a user using an electroencephalogram comprises of the following steps: at step 202, extracting neurological signals from a brain of the user using an acquisition module comprising of a wearable electroencephalogram (EEG) module, wherein the wearable EEG module comprises of a plurality of electrodes positioned on a surface of the wearable EEG module and touching a scalp of the user for extracting the neurological signals from the brain; at step 204, processing the neurological signal using a signal processing module connected to the acquisition module, wherein the processing of neurological signal comprises of the following steps: at step 206, recording event related potentials using a feature extraction module based on the neurological signals extracted from the brain during a particular task, wherein an amplitude and latency of event related potential is extracted from the neurological signals; and at step 208, classifying the mental ability of the user using a classification module connected to the feature extraction module, wherein the classification module uses a spiking neural network having a 3-fold classification technique.

This disclosure can be understood from Figure 3a. It shows a subject (10) who has worn an EEG cap on its scalp with EEG electrodes (11). The electrodes are placed according to the 10-20 electrode placement system followed internationally. In an embodiment, the EEG acquisition has been performed from nine electrodes placed on position Fp1, Fp2, Fz, P3, P4, Pz, 01, 02, and Oz. The electrode impedance has been kept to 0 to 10 KOhm. The electrodes measure the brain signals using an amplifier (12) and transfer it to a computer system (13). The recording of EEG signals is simultaneously performed while presenting the set of stimuli to the subject in the form of a video on the system (14). The subject (10) responds to the stimuli or questions asked by saying "yes" or "no". The tasks presented here will analyse the visual memory, speed of learning, knowledge of the language, rate of response etc. The task is presented for five trials which can be increased. Also, the recording of eye close and eye open of the subject (10) has been performed. Before the experiment is performed the subject (10) is given training so that they are comfortable with hardware and are familiar with the sessions.

The task performed here will analyse some of the cognitive functions of the subject such as learning rate, attention, rate of response, impulsiveness etc. A series of trials are conducted, to present the task-related stimuli to the subject. During the presentation of stimuli, EEG data is recorded. EEG data has also been recorded for eye open and close while no task is performed. It is done to perform the comparison between these recorded EEG signals. The task-related stimuli are presented on a laptop or computer screen in the form of a video or images. The subject has to respond to the stimuli using a keyboard or mouse or any other input device. One computer device is connected to the EEG device through an amplifier on which EEG signals are displayed and recorded.

The test has been performed under normal conditions. The test involves a presentation on some set of images for the training set and the same images with some additional random images to test the memory of the subject. For training 10 images will be presented to the subject. During testing phase 10 training images with 20 test images are randomly presented to the subject. Each image will be presented for 3 seconds. One session of recording is performed for eye close and eye open. For eye open a blank screen is presented for 5 seconds where the subject will not be performing any task. For eye close subject will be provided with an auditory stimulus when the recording is starting and auditory stimuli at the end. The duration of the recording for eye close will be of 5 seconds.

In another session to test the subject's basic knowledge about words, a word is presented with its synonym and an antonym. The subject has to identify the correct pair among the synonym or antonym. Each word pair is presented for 3 seconds and in total 20 word pairs (synonym or antonym) are presented. In a yet another session to test the subject's speed and accuracy to response, digits from 0 to 9 are randomly presented with one number is missing or is given twice. The subject has to identify the missing digit or the digit presented twice. Each task is presented for 3 seconds and 20 number series are presented.

In an embodiment, the tests are presented on a computer screen, recorded using an EEG device connected to another computer. The recorded EEG signals are saved offline for further analysis and execution of procedures or algorithms on data. The test presented here can be varied and can be of more difficulty level or less difficulty level to measure the mental ability of the subject undergoing the test. Or in some embodiments, the level of questions can be from easy to high to track the performance of the person. In other embodiments, the analysis of data recorded is performed. Before data analysis, data is bandpass filtered from a range of 0.5 Hz to 30 Hz using a bandpass filter. Each subject data is analysed separately.

Figure 3b presents the data analysis module of the invention. After the acquisition, the EEG data is converted into numerical attributes and signal processing (21) is carried out. The signal processing module (21) involves data filtering and feature extraction.

The series of presentation of stimuli is given in Figure 3c. The stimuli are presented on the system (14) which is kept at a safe distance and invisibility range of the subject.

The extracted EEG data with wavelet transform is fed to module 22. In an embodiment signal classification includes an algorithm which will perform classification of EEG data. In an embodiment, a classification module (22) uses a spiking neural network to classify the subject mental ability. The classification results using SNN are given in Figure 3d. According to an embodiment, the average accuracy of the three-class classification is 83.95. The results displayed here are performed using 5-fold cross-validation of all subject data.

The common spatial pattern has been utilized as a feature extraction approach. Other approaches like wavelet transform, fast Fourier transform, independent component analysis can be variably applied on EEG data. The event-related potentials (ERP) recorded after the stimuli presentation has been given in figure 4a, 4b, 4c and 4d. ERPs are the potential elicited after the occurrence of stimuli as stated in the literature. There are various components of ERP such as N100, P300, P200, N400 etc. which occur at different times. They depend on the responses by subject to the presented tasks or stimulus. The amplitude and latency of ERP change with tasks and hence show the behaviour, awareness, accuracy, and capacity of the subject towards the task.

Figure 5, illustrates the filtered EEG data and original EEG data of a subject for few time samples in accordance with an embodiment of the present disclosure. EEG data has been filtered using a Hamming windowed sine FIR filter which has been represented in Figure 5.

The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.

Claims

WE CLAIM

1. A system for assessing mental ability of a user using an electroencephalogram, the system comprises of:

an acquisition module comprising of a wearable electroencephalogram (EEG) module for extracting neurological signals from a brain of the user, wherein the wearable EEG module comprises of a plurality of electrodes positioned on a surface of the wearable EEG module and touching a scalp of the user for extracting the neurological signals from the brain;

a signal processing module connected to the acquisition module for processing the neurological signal, wherein the processing module comprises of:

a feature extraction module for recording event related potentials based on the neurological signals extracted from the brain during a particular task, wherein an amplitude and latency of event related potential is extracted from the neurological signals; and

a classification module connected to the feature extraction module for classifying the mental ability of the user, wherein the classification module uses a spiking neural network having a 3-fold classification technique.

2. The system as claimed in claim 1, wherein a computing module is connected to the EEG module for analysing and displaying the neurological signals from the brain using an amplifier.

3. The system as claimed in claim 1, wherein a data filtration module is connected to the processing module for filtering a range of about 0.5Hz to 30Hz using a band pass filter.

4. The system as claimed 1, wherein the acquisition system includes an EEG wearable cap, the metal electrodes, and the amplifier, wherein the data analysis module involves the signal filtering module, the feature extraction module and the classification module.

5. The system as claimed in claim 1, wherein the brain activity of the brain is recorded and associated with the responses created by a neuron and transferred to other neurons through a synapse.

6. The system as claimed in claim 1, wherein the wearable EEG cap has about 16 electrodes and placed on the scalp in a 10-20 arrangement of electrodes to monitor the behavior of the brain signals which is fluctuated by the peripheral activities.

7. The system as claimed in claim 1, wherein the EEG acquisition is performed from nine electrodes placed on position Fpl, Fp2, Fz, P3, P4, Pz, 01, 02, and Oz, wherein an impedance of electrode is kept to 0 to 10 KOhm.

8. The system as claimed in claim 1, wherein a plurality of tasks is formed to analyze the visual memory, speed of learning, knowledge of the language, rate of response etc., wherein the plurality of tasks analyses cognitive functions of the subject such as learning rate, attention, rate of response, impulsiveness.

9. The system as claimed in claim 1, wherein the EEG data is filtered using a Hamming windowed sine FIR filter.

10. A method for assessing mental ability of a user using an electroencephalogram, the method comprises of:

extracting neurological signals from a brain of the user using an acquisition module comprising of a wearable electroencephalogram (EEG) module, wherein the wearable EEG module comprises of a plurality of electrodes positioned on a surface of the wearable EEG module and touching a scalp of the user for extracting the neurological signals from the brain;

processing the neurological signal using a signal processing module connected to the acquisition module, wherein the processing of neurological signal comprises of:

recording event related potentials using a feature extraction module based on the neurological signals extracted from the brain during a particular task, wherein an amplitude and latency of event related potential is extracted from the neurological signals; and classifying the mental ability of the user using a classification module connected to the feature extraction module, wherein the classification module uses a spiking neural network having a 3-fold classification technique.

Extracting neurological signals from a brain of the user using an acquisition module comprising of a wearable electroencephalogram (EEG) module, wherein the wearable EEG module comprises of a plurality 202 of electrodes positioned on a surface of the wearable EEG module and touching a scalp of the user for extracting the neurological signals from the brain.

Processing the neurological signal using a signal processing module connected to the acquisition module, wherein the processing of neurological signal comprises of the following: 204

Recording event related potentials using a feature extraction module based on the neurological signals extracted from the brain during a particular task, wherein an amplitude and latency of event related potential is extracted from the neurological signals. 206

Classifying the mental ability of the user using a classification module connected to the feature extraction module, wherein the classification module uses a spiking neural network having a 3-fold classification 208 technique.

Figure 2

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