Enhancing Understanding of Driving Attributes through Quantitative Assessment of Driver Cognition

This paper presents a novel approach for analysing EEG data from drivers in a simulated driving test. We focused on the Hurst exponent, Shannon entropy, and fractal dimension as markers of the nonlinear dynamics of the brain. The results show significant trends: Shannon Entropy and Fractal Dimension exhibit variations during driving condition transitions, whereas the Hurst exponent reflects memory retention portraying learning patterns. These findings suggest that the tools of Non-linear Dynamical (NLD) Theory as indicators of cognitive state and driving memory changes for assessing driver performance and advancing the understanding of non-linear dynamics of human cognition in the context of driving and beyond. Our study reveals the potential of NLD tools to elucidate brain state and system variances, enabling their integration into current Deep Learning and Machine Learning models. This integration can extend beyond driving applications and be harnessed for cognitive learning, thereby improving overall productivity and accuracy levels

Design, Fabrication and Experimental Validation of a Novel Dry-Contact Sensor for Measuring Electroencephalography Signals without Skin Preparation

In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain low skin-sensor interface impedance. Additionally, the flexible substrate also initiates a sensor buffer effect, eliminating pain when force is applied. The proposed dry EEG sensor was reliable in measuring EEG signals without any skin preparation or conductive gel usage, as compared with the conventional wet electrodes

Affective Computing in the Area of Autism

The prevalence rate of Autism Spectrum Disorders (ASD) is increasing at an alarming rate (1 in 68 children). With this increase comes the need of early diagnosis of ASD, timely intervention, and understanding the conditions that could be comorbid to ASD. Understanding co-morbid anxiety and its interaction with emotion comprehension and production in ASD is a growing and multifaceted area of research. Recognizing and producing contingent emotional expressions is a complex task, which is even more difficult for individuals with ASD. First, I investigate the arousal experienced by adolescents with ASD in a group therapy setting. In this study I identify the instances in which the physiological arousal is experienced by adolescents with ASD ( have-it ), see if the facial expressions of these adolescents indicate their arousal ( show-it ), and determine if the adolescents are self-aware of this arousal or not ( know-it ). In order to establish a relationship across these three components of emotion expression and recognition, a multi-modal approach for data collection is utilized. Machine learning techniques are used to determine whether still video images of facial expressions could be used to predict Electrodermal Activity (EDA) data. Implications for the understanding of emotion and social communication difficulties in ASD, as well as future targets for intervention, are discussed. Second, it is hypothesized that a well-designed intervention technique helps in the overall development of children with ASD by improving their level of functioning. I designed and validated a mobile-based intervention designed for teaching social skills to children with ASD. I also evaluated the social skill intervention. Last, I present the research goals behind an mHealth-based screening tool for early diagnosis of ASD in toddlers. The design purpose of this tool is to help people from low-income group, who have limited access to resources. This goal is achieved without burdening the physicians, their staff, and the insurance companies

Characterizing the Noise Associated with Sensor Placement and Motion Artifacts and Overcoming its Effects for Body-worn Physiological Sensors

Wearable sensors for continuous physiological monitoring have the potential to change the paradigm for healthcare by providing information in scenarios not covered by the existing clinical model. One key challenge for wearable physiological sensors is that their signal-to-noise ratios are low compared to those of their medical grade counterparts in hospitals. Two primary sources of noise are the sensor-skin contact interface and motion artifacts due to the user’s daily activities. These are challenging problems because the initial sensor placement by the user may not be ideal, the skin conditions can change over time, and the nature of motion artifacts is not predictable. The objective of this research is twofold. The first is to design sensors with reconfigurable contact to mitigate the effects of misplaced sensors or changing skin conditions. The second is to leverage signal processing techniques for accurate physiological parameter estimation despite the presence of motion artifacts. In this research, the sensor contact problem was specifically addressed for dry-contact electroencephalography (EEG). The proposed novel extension to a popular existing EEG electrode design enabled reconfigurable contact to adjust to variations in sensor placement and skin conditions over time. Experimental results on human subjects showed that reconfiguration of contact can reduce the noise in collected EEG signals without the need for manual intervention. To address the motion artifact problem, a particle filter based approach was employed to track the heart rate in cardiac signals affected by the movements of the user. The algorithm was tested on cardiac signals from human subjects running on a treadmill and showed good performance in accurately tracking heart rate. Moreover, the proposed algorithm enables fusion of multiple modalities and is also computationally more efficient compared to other contemporary approaches

Linking Insight To Behaviour Change In A Life Coaching Intervention For Women

Solving a problem with insight provokes a change of mind and concomitantly, behaviour. This mixed method study examined moments of insight during life coaching to determine whether having moments of insight led to more meaningful and sustained behavior change. Moments of insight and non-insight were tracked over nine life-coaching sessions with a population of women (N=6) and their coaches (N=6). Validated measures of problem-solving ability, psychological well-being, and mindfulness were collected before and after the intervention, along with behaviour change goals, Wheel of Life® satisfaction, and a personal strength profile. At eight weeks post intervention, sustainability was assessed via an online survey. Insights increased significantly (

Evaluating a Novel Brain-Computer Interface and EEG Biomarkers For Cognitive Assessment in Children With Cerebral Palsy

Standardized neuropsychological assessments and research instruments are typically administered with verbal queries, pictures and manipulatives that require verbal or motor responses. Thus, they are often inaccessible to people with physical and communicative impairments. The goal of this dissertation was to investigate alternative approaches that do not require any motor or speech input to assess cognitive capacity of an individual. The first approach involved using a brain-computer interface (BCI) that was adapted to facilitate the administration of a Peabody Picture Vocabulary Test (PPVT-IV). Which is a receptive vocabulary assessment than can be used as a proxy for intelligence. The second approach was to use brain dynamics such as functional connectivity and bandpass analysis to assess cognitive capacity of an individual. We then tested these two approaches on typically developing (TD) individuals (N=11) people with cerebral palsy (CP) (N=18). Our results suggest that children with cerebral palsy show signs of lower intelligence than typically developing children when using functional connectivity and power band analysis, however, they performed equally well in the PPVT-IV. We believe this is due to the neural compensation resulting from the subjects’ pathology. Thus, the preferred method for assessing cognitive measures in an individual with severe motoric impairments is a BCI. By using a BCI, a user can respond to standardized cognitive assessments that already have well established norms. However, it is important to make sure that when designing these systems, the changes made to adapt the cognitive assessment for the BCI do not alter the format and psychometrics of the test. Our BCI able to maintain the psychometrics of a PPVT-IV test and perform with an accuracy of 97.78 ± 4.06. In addition, scores on the BCI-facilitated PPVT-IV and the standard PPVT-IV were highly correlated (r = 0.95, p<0.001) with a mean difference of 2.0 ± 6.4 points, which is within the standard error of the PPVT-IV. Thus, our BCI-facilitated PPVT-IV provided comparable results to the standard PPVT-IV, suggesting that populations for whom standardized cognitive tests are not accessible could benefit from our BCI-facilitated approach.PHDNeuroscienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136949/1/pharoram_1.pd

Ein neues Konzept zur Sensorik und Steuerung einer aktiven Hybrid-Orthese für die obere Extremität

Für die Nutzung der aktiven Hybrid-Orthese für die obere Extremität wird in dieser Dissertationsschrift ein neues Konzept zur Sensorik und Steuerung vorgestellt. Die Grundidee besteht darin, neue, beziehungsweise modifizierte, Sensoren zur Steuerung der aktiven Hybrid-Orthese zu entwickeln. Mittels der entwickelten Sensoren wird eine neue Steuerung und ein neues Konzept zur Steuerung der aktive Hybrid-Orthese entwickelt und anschließend evaluiert

Development of a compact, low-cost wireless device for biopotential acquisition

A low-cost circuit board design is presented, which in one embodiment is smaller than a credit card, for biopotential (EMG, ECG, or EEG) data acquisition, with a focus on EEG for brain-computer interface applications. The device combines signal conditioning, low-noise and high-resolution analog-to-digital conversion of biopotentials, user motion detection via accelerometer and gyroscope, user-programmable digital pre-processing, and data transmission via Bluetooth communications. The full development of the device to date is presented, spanning three embodiments. The device is presented both as a functional data acquisition system and as a template for further development based on its publicly-available schematics and computer-aided design (CAD) files. The design will be made available at the GitHub repository https://github.com/kellygs/eeg

Development and implementation of multimodal system for attention monitoring in naturalistic work environments.

Kako tehnologija stalno napreduje, industrijske nesreće koje se vezuju za neispravnost tehničkih sistema su skoro skroz umanjene. Iz tog razloga, ljudska greška se smatra uzročnikom oko 80% nesreća u industriji. Jedan od glavnih uzročnika ljudske greške je limitirana mentalna izdržljivost ljudskih operatera, koja uzrokuje pad u pažnji radnika i posledično vodi do grešaka u radu. Klasične ergonomske metode koje se koriste za procenu kognitivnog stanja čoveka su uglanom kvalitativne i subjektivne, i prema tome su prilično nepouzdane. Iz tog razloga, psihofiziološki senzori su počeli da se primenjuju u ergonomiskim istraživanjima, sa ciljm da obezbede objektivne i kvantitativne mere radnikovog kognitivnog stanja. Prateći taj trend, neuroergonomija se pojavila kao naučna pod-disciplina ergonomije. Prednost korišćenja neuroergonomskih metoda, je u tome što neuroergonomija istražuje funkcionalnu zavisnost između dinamike mozga i bihevioralnih parametara i tako zaobilazi teoretske veze koje opisuju korelaciju između ovih parametara, a koje su korišćene u ergonomiji. Ova disertacija predstavlja naučni okvir za multimodalni sistem koji je predložen da se koristi za praće pažnje radnika i koji koristi psihofiziološke senzore i bihevijoralna merenja. Sistem se sastoji od psihofizioloških senzora, kao što su: galvanski reakciju kože (eng. galvanic skin response - GSR), merenje otkucaja srca (eng. heart rate –HR) i elekroencefalografiju (eng. Electroencephalography – EEG); bihevioralne modalitete kao što su: Vremena reakcija (eng. reaction times – RTs) i senzore za praćenje pokreta (eng. motion capture – MoCap), “Kinect” the “Leap Motion”. Iako je predstavljen okvir za snimanje pomenutih modaliteta u realnom vremenu, ova disertacija je fokusirana na rezultate koji su dobijeni snimanjem EEG, RTs i Kinect modaliteta. Glavni cilj disertacije je istraživanje mogućnosti korišćenja savremenog prenosnog EEG-a u industrijskim uslovima, sa ciljem praćenja pažnje radnika. Prethodna istraživanja koja su koristila EEG su bila uglavnom obavljana u kontrolisanim laboratorijskim uslovima i zbog toga, nalazi iz tih studija se uzimaju sa određenom dozom rezerve. Da bi se snimio EEG u realnom radnom okruženju, radno mesto u kojem radnici sklapaju hidraulično crevo je verodostojno replicirano i subjekti u studiji su simulirali taj proces. Disertacija se sastoji od četiri eksperimentalne studije. U prvoj studiji, ispitivano je kako česte mikro-pauze utiču na nivo pažnje radnika, poredeći amplitude P300 Komponente evociranih kognitivnih potencijala (eng. event-related potential – ERP) pre i neposredno posle perioda mikro-pauze. Glavni nalaz je da mikro-pauze pozitivno utiču na nivo pažnje radnika i predloženo je njihovo uključenje u dnevne aktivnosti radnika. U drugoj studiji, istraživano je da li radnici imaju veći nivo pažnje ukoliko im je nametnuto sa kojom rukom da počnu sklapanje creva. Dve psihološke paradigme su bile predstavljene učesnicima u studiji, paralelno sa simuliranm akcijom sklapanja creva. U prvoj paradigmi, učesnici su mogli da izaberu da otpočnu operaciju sa bilo kojom rukom, dok su u drugoj bili uslovljeni da započnu operaciju rukom koja odgovara smeru strelice koja se prikazivala na ekranu ispred njih. Ovo istraživanje je otkrilo da su učesnici imali veći nivo pažnje u slučaju uslovljavanja kojom rukom da započnu operaciju, jer je amplituda P300 komponente bila značajno viša u poređenju sa slučajem kada su mogli slobodno da izaberu sa kojom rukom će započinjati zadatak.As technology is ever advancing, industrial accidents related to technological malfunctioning have been almost diminished, leaving the human error responsible for up to 80% of the remaining accidents. One of the main causes for this is limited mental endurance of human operators’, which causes the attention decline and consequently leads to an operating error. Classical ergonomics methods for assessing the operators’ cognitive state are still dependent on the subjective and qualitative methods, thus being unreliable. For that reason, in the recent years the psychophysiological sensors were included in the ergonomics research, with the aim of providing the objective and quantitative measures of the operators’ cognitive state. Following that path, the neuroergonomics emerged as a scientific discipline, which investigates the human brain functions in relation to performance at work. The advantage of using neuroergonomics is that it investigates the functional relationship between brain dynamics and behavioral parameters, thus avoiding theoretical constructs that describe the correlation between these two, and which are ubiquitously used in ergonomics research. The present dissertation introduces a framework for the multimodal attention monitoring system, utilizing psychophysiological and behavioral measurements. The multimodal system consists of psychophysiological sensors, such as galvanic skin response (GSR), heart rate (HR) sensor and electroencephalography (EEG), the behavioral modality of the reaction times (RTs), and the motion capture (MoCap) sensors Kinect and the Leap Motion. Although the framework for synchronous and real-time recording for all the sensors was provided, this thesis was focused solely on the results obtained from the EEG, RTs and Kinect recordings. The main aim of the presented dissertation is to investigate the possibility of utilization of the recently available wearable electroencephalography (EEG) in industrial setting, with the goal of the operator’s attention monitoring. Previously reported EEG studies that were concerned with the attention states of the operators were mainly confined to the strictly controlled laboratory conditions and therefore, the findings from these studies needed to be taken with the certain ambiguity. In order to record the EEG in naturalistic environment, specific workplace where operators’ assembly the hoses, used in hydraulic break systems in vehicles, was faithfully replicated and the participants in the studies simulated the manual assembly operations. The present dissertation consists of four experimental studies, where the first two were concerned with investigation how different work conditions influence the cognitive state of the operators’, i.e. the studies were concerned with the assembly task design. In the first study, the influence of the frequent micro-breaks on the cognitive state of the workers’ was investigated, by comparing the P300 event-related potential (ERP) amplitude prior and immediately following the micro-break period. It was found that the micro-breaks enhance the attention of the operators’ and the proposal for their inclusion in the regular work routine was made. Second study investigated the influence of hand alteration on the attention level of the operators’. For that aim, the participants in the study were presented with two distinct task: the one in which they could initiate the assembly operation with whichever hand they preferred, and the one in which they were conditioned with which hand they should initiate the operation. This study revealed that the instructed hand responding induces the higher attention, as assessed through the P300 component’s amplitude, compared to the experimental condition where the participants could freely choose the hand for the initiation of the assembly operation. Further, a framework for the on-line assessment of the operators’ cognitive state was provided. In the third experimental study, the propagation of the P300 component’s amplitude was observed and correlated with the RTs. On the group level, a negative correlation was found, confirming the previously reported finding. However, due to individual differences, the correlation on the individual level was inconsistent, emphasizing the necessity for the individualized EEG measurements for the reliable attention monitoring system. Finally, it was investigatetd whether the quantity of task unrelated movements corresponds to attention of the operator, as previously shown to be negatively related to the attention of operators’. For that aim, the concept of movement energy (ME) was introduced and correlated with EEG attention-related modalities. The initial finding from this study showed that the ME is negatively related to the EEG attention-related modalities and proved that the future attention monitoring system can be built based on these modalities