An evaluation of real-time cognitive state classification in a harsh operational environment

2007 ◽  
Author(s):  
Michael C. Dorneich ◽  
Santosh Mathan ◽  
Patricia May Ververs ◽  
Stephen D. Whitlow
Keyword(s):  
Real Time ◽  
Cognitive State ◽  
Operational Environment ◽  
State Classification

An Evaluation of Real-Time Cognitive State Classification in a Harsh Operational Environment

2007 ◽  
Vol 51 (4) ◽  
pp. 146-150 ◽  
Author(s):  
Michael C. Dorneich ◽  
Santosh Mathan ◽  
Patricia May Ververs ◽  
Stephen D. Whitlow
Keyword(s):  
Real Time ◽  
Rating Scales ◽  
Assessment Tools ◽  
Cognitive State ◽  
Operational Environment ◽  
Task Load ◽  
Augmented Cognition ◽  
State Classification ◽  
The Individual ◽  
Cognitive Monitoring

This paper describes an evaluation conducted with a full platoon of 32 Soldiers at Aberdeen Proving Grounds' MOUT site in Aberdeen, MD. The objective was to assess the cognitive workload classification techniques driven by neuro-physiological (EEG) and physiological (ECG) sensors. In a first ever evaluation of real-time cognitive monitoring in the harsh operational environment, the assessment culminated in a three phase, 24-hour mission consisting of a coordinated Route Reconnaissance, a Cordon and Search of a village, and a Hasty Defense operation. Task load levels were manipulated by introducing unexpected and unplanned events requiring re-planning and extensive coordination by the leadership (high task load) as well as lulls in the activity in which part missions were executed flawlessly with little variations on the preplanned, well versed drill (low task load). Four leaders (Platoon Leader, Platoon Sergeant, Squad Leader 1, and Squad Leader 2) were equipped with sensors to measure and output cognitive state in real-time. The fused EEG and ECG workload classification approach reached 95% accuracy depending on the individual and the amount of data used to train the classifier. This level of success implies that Augmented Cognition workload assessment tools enable the ability to move beyond subjective workload rating scales, such as NASA TLX and Cooper Harper ratings, to more objective measurements of real-time cognitive state metrics in almost any conceivable operational environment.

scholarly journals A framework for closed-loop neurofeedback for real-time EEG decoding

2019 ◽  
Author(s):  
Greta Tuckute ◽  
Sofie Therese Hansen ◽  
Troels Wesenberg Kjaer ◽  
Lars Kai Hansen
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Brain Activity ◽  
Time Signal ◽  
Software Framework ◽  
Cognitive State ◽  
Scalp Eeg ◽  
State Classification ◽  
Dry Electrode ◽  
Wide Range

AbstractNeurofeedback based on real-time brain imaging allows for targeted training of brain activity with demonstrated clinical applications. A rapid technical development of electroen-cephalography (EEG)-based systems and an increasing interest in cognitive training has lead to a call for accessible and adaptable software frameworks. Here, we present and outline the core components of a novel open-source neurofeedback framework based on scalp EEG signals for real-time neuroimaging, state classification and closed-loop feedback.The software framework includes real-time signal preprocessing, adaptive artifact rejection, and cognitive state classification from scalp EEG. The framework is implemented using exclusively Python source code to allow for diverse functionality, high modularity, and easy extendibility of software development depending on the experimenter’s needs.As a proof of concept, we demonstrate the functionality of our new software framework by implementing an attention training paradigm using a consumer-grade, dry-electrode EEG system. Twenty-two participants were trained on a single neurofeedback session with behavioral pre- and post-training sessions within three consecutive days. We demonstrate a mean decoding error rate of 34.3% (chance=50%) of subjective attentional states. Hence, cognitive states were decoded in real-time by continuously updating classification models on recently recorded EEG data without the need for any EEG recordings prior to the neurofeedback session.The proposed software framework allows a wide range of users to actively engage in the development of novel neurofeedback tools to accelerate improvements in neurofeedback as a translational and therapeutic tool.

Supporting Real-Time Cognitive State Classification on a Mobile Individual

2007 ◽  
Vol 1 (3) ◽  
pp. 240-270 ◽  
Author(s):  
Michael C. Dorneich ◽  
Stephen D. Whitlow ◽  
Santosh Mathan ◽  
Patricia May Ververs ◽  
Deniz Erdogmus ◽  
...  
Keyword(s):  
Real Time ◽  
Cognitive State ◽  
State Classification

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

scholarly journals Channel Selection and Feature Projection for Cognitive Load Estimation Using Ambulatory EEG

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Tian Lan ◽  
Deniz Erdogmus ◽  
Andre Adami ◽  
Santosh Mathan ◽  
Misha Pavel
Keyword(s):  
Mutual Information ◽  
Cognitive Load ◽  
Channel Selection ◽  
Estimation Accuracy ◽  
Cognitive State ◽  
Load Estimation ◽  
Mental Load ◽  
Cognitive States ◽  
State Classification ◽  
Feature Projection

We present an ambulatory cognitive state classification system to assess the subject's mental load based on EEG measurements. The ambulatory cognitive state estimator is utilized in the context of a real-time augmented cognition (AugCog) system that aims to enhance the cognitive performance of a human user through computer-mediated assistance based on assessments of cognitive states using physiological signals including, but not limited to, EEG. This paper focuses particularly on the offline channel selection and feature projection phases of the design and aims to present mutual-information-based techniques that use a simple sample estimator for this quantity. Analyses conducted on data collected from 3 subjects performing 2 tasks (n-back/Larson) at 2 difficulty levels (low/high) demonstrate that the proposed mutual-information-based dimensionality reduction scheme can achieve up to 94% cognitive load estimation accuracy.

scholarly journals Acquisition and Analysis System of the Ballistocardiogram Signal Based on Virtual Instruments

2011 ◽  
Vol 2-3 ◽  
pp. 595-598
Author(s):  
Fang Fang Jiang ◽  
Xu Wang ◽  
Dan Yang ◽  
Yu Hao
Keyword(s):  
Heart Rate ◽  
Cardiac Function ◽  
Real Time ◽  
High Efficiency ◽  
Virtual Instruments ◽  
Invasive Technique ◽  
Operational Environment ◽  
Respiration Frequency ◽  
Non Invasive ◽  
Analysis System

Ballistocardiogram signal (BCG) is a non-invasive technique for the assessment of the cardiac function. It consists mainly of heart movement and the movement of blood in aorta, arteries, and periphery, which can be used to real-time monitor the heart rate and respiration frequency at home. In our laboratory, a sitting BCG detection chair has been designed successfully, and the acquisition and analysis system based on virtual instruments is proposed in this paper. MATLAB7.0 and LabVIEW8.5 were used to simulate the operational environment, and the results show high efficiency and accuracy in displaying waveform and spectrum, extracting main characteristics of heart rate and respiratory frequency, and alerting when abnormal heart-rate occurs.

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