Kognition arviointi : Hajautetun tiedon sovelluksia

The amount of information collected by personal health records, smartphone ecosystems, and other cloud services has increased enormously in recent years. This has, for instance, led to new ways of automated physical exercise assessment, but this also introduces the potential for novel methods and applications in the automated evaluation of various mental factors such as cognitive state and stress. Extracting such latent variables holds considerable promise, in particular in group-level analysis. Furthermore, the current initiatives and research programs collecting masses of health data from large cohorts create opportunities for developing the methodology. The lack of targeted research is partially hindering the development of the analysis of obscure factors, progress of machine learning and other algorithmic solutions, and the evolution of novel applications and technologies. As described in this introduction, various features inherent in biosignals increase the complexity in the research. In this thesis I provide an introduction to the emerging ubiquitous recording of physiological signals, its effects on the industry, opportunities for organizations and management, and data analytics and measurement techniques. The aim is to seek the future prospects of systemic scenarios and large-scale initiatives. The original publications reviewed in this thesis seek biosignals for features responsive for cognitive states such as stress and, more interestingly, second-order factors derived from inter-individual responses and activations. By introducing more complex features to psychophysiological research, group analytics can be further developed. Second-order analyses provide robust signal features and may lead to advanced applications in assessing well-being and performance. The original publications consist of three research articles and a primer review. The experiments include recordings of magnetoencephalography (MEG), heart rate variability (HRV), and electrodermal activity (EDA), and they exemplify systemic use cases of biosignals. The included primer review discusses general methods in psychophysiological research in human–computer interaction (HCI). Together with this introduction, my experimental results provide evidence that taking psychophysiological measurements from the laboratory to ecologically valid environments is plausible and effective. The literature suggests that consumer-grade devices and personal internet of things will revolutionize myriad sectors, e.g., organizational management. Together with an exponentially increasing data collection and novel applications, the industry will have large economical impacts.Henkilökohtaisen terveystiedon kerääminen ja tallennus on lisääntynyt valtavasti viime vuosina. Monet käyttävät tietoa esimerkiksi fyysisen harjoittelun tukena. Tämän lisäksi mitattua tietoa on alettu hyödyntää esimerkiksi stressitilojen tunnistamisessa. Tällaista fysiologisten signaalien arviointia kutsutaan psykofysiologiaksi. Jatkokehityksen avulla tällaiset piirteet sopivat varsinkin ryhmäanalyyseihin ja suurempien joukkojen arvioimiseen. Menetelmien kehitystä tukevat useat suuret väestötason tutkimusavaukset. Toisaalta juuri kohdennetun tutkimuksen puute osaltaan hidastaa tallennetusta tiedosta eristettävien piilevien piirteiden hyödyntämisen yleistymistä uusissa algoritmeissa ja sovel- luksissa. Tässä yhteenvedossa esittelen, mitkä asiat vaikuttavat osaltaan tähän kehitykseen. Esittelen fysiologisten signaalien mittaamisen taustoja, sekä mittausmenetelmien kehitystä. Lisäksi pohdin kaupallisten sovellusten mahdollisuuksia ja muita tulevaisuuden näkymiä. Johdanto-osuus toimii siten taustamateriaalina soveltavalle osiolle ja liitetyille osajulkaisuille. Osajulkaisut tutkivat kohdennetummin biosignaalien soveltuvuutta kognitiivisen toim- intakyvyn arvioimisessa. Jäljemmät julkaisut keskittyvät useiden yksilöiden biosignaalien kovarianssia hyödyntäviin menetelmiin. Tällaiset menetelmät luovat pohjaa kehittyneem- mille analyysitavoille ja signaalien yhä tehokkaammalle hyödyntämiselle hyvinvoinnin ja toimintakyvyn arvioinnissa. Kolme ensimmäistä osajulkaisua ovat kokeellisia tutkimusar- tikkeleita ja viimeinen on katsaus olemassa olevaan tutkimukseen. Tutkimusasetelmissa hyödynnetyt fysiologiset menetelmät ovat magnetoenkefalografia (MEG), sykevälivaihtelu (HRV) ja ihosähköinen vaste (EDA). Katsaus toisaalta tarkastelee psykofysiologian hyödyn- tämistä tietokoneen käyttöliittymätutkimuksessa (HCI). Yhdessä tämän yhteenvedon kanssa tutkimustulokset edistävät mittausmenetelmien hyödynnettävyyttä luonnollisissa ympäristöissä, sekä psykofysiologisten signaalien käyttöä vaihtelevissa ja kontrolloimattomissa olosuhteissa. Kirjallisuudesta löytyy viitteitä kulutta- jalaitteiden ja esineiden internetin kasvusta ja potentiaalista mullistaa useita sektoreita, kuten organisaatioiden ohjaus. Lähteet ennustavat myös markkinoiden kasvua. Yhdessä kaikkialle levittyvä tiedon kerääminen ja uudet sovellukset sekä datalähtöiset analyysimenetelmät voivat johtaa suuriin muutoksiin

Electrophysiological investigations of brain function in coma, vegetative and minimally conscious patients.

Electroencephalographic activity in the context of disorders of consciousness is a swiss knife like tool that can evaluate different aspects of cognitive residual function, detect consciousness and provide a mean to communicate with the outside world without using muscular channels. Standard recordings in the neurological department offer a first global view of the electrogenesis of a patient and can spot abnormal epileptiform activity and therefore guide treatment. Although visual patterns have a prognosis value, they are not sufficient to provide a diagnosis between vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) patients. Quantitative electroencephalography (qEEG) processes the data and retrieves features, not visible on the raw traces, which can then be classified. Current results using qEEG show that MCS can be differentiated from VS/UWS patients at the group level. Event Related Potentials (ERP) are triggered by varying stimuli and reflect the time course of information processing related to the stimuli from low-level peripheral receptive structures to high-order associative cortices. It is hence possible to assess auditory, visual, or emotive pathways. Different stimuli elicit positive or negative components with different time signatures. The presence of these components when observed in passive paradigms is usually a sign of good prognosis but it cannot differentiate VS/UWS and MCS patients. Recently, researchers have developed active paradigms showing that the amplitude of the component is modulated when the subject's attention is focused on a task during stimulus presentation. Hence significant differences between ERPs of a patient in a passive compared to an active paradigm can be a proof of consciousness. An EEG-based brain-computer interface (BCI) can then be tested to provide the patient with a communication tool. BCIs have considerably improved the past two decades. However they are not easily adaptable to comatose patients as they can have visual or auditory impairments or different lesions affecting their EEG signal. Future progress will require large databases of resting state-EEG and ERPs experiment of patients of different etiologies. This will allow the identification of specific patterns related to the diagnostic of consciousness. Standardized procedures in the use of BCIs will also be needed to find the most suited technique for each individual patient.Peer reviewe

MODIFICATION AND EVALUATION OF A BRAIN COMPUTER INTERFACE SYSTEM TO DETECT MOTOR INTENTION

It is widely understood that neurons within the brain produce electrical activity, and electroencephalography—a technique used to measure biopotentials with electrodes placed upon the scalp—has been used to observe it. Today, scientists and engineers work to interface these electrical neural signals with computers and machines through the field of Brain-Computer Interfacing (BCI). BCI systems have the potential to greatly improve the quality of life of physically handicapped individuals by replacing or assisting missing or debilitated motor functions. This research thus aims to further improve the efficacy of the BCI based assistive technologies used to aid physically disabled individuals. This study deals with the testing and modification of a BCI system that uses the alpha and beta bands to detect motor intention by weighing online EEG output against a calibrated threshold

Preliminary Results on a New Algorithm for Blink Correction Adaptive to Inter- and Intra-Subject Variability

This paper presents a new preprocessing method to correct blinking artifacts in Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs). This Algorithm for Blink Correction (ABC) directly corrects the signal in the time domain without the need for additional Electrooculogram (EOG) electrodes. The main idea is to automatically adapt to the blink's inter- and intra-subject variability by considering the blink's amplitude as a parameter. A simple Minimum Distance to Riemannian Mean (MDRM) is applied as the classification algorithm. Preliminary results on three subjects show a mean classification accuracy increase of 13.7% using ABC

14 challenges for conducting social neuroscience and longitudinal EEG research with infants

The use of electroencephalography (EEG) to study infant brain development is a growing trend. In addition to classical longitudinal designs that study the development of the neural, cognitive and behavioural function, new areas of EEG application are emerging, such as novel social neuroscience paradigms using dual infant-adult EEG recordings. However, most of the experimental designs, analysis methods, as well as EEG hardware were originally developed for single-person adult research. When applied to the study of infant development, adult-based solutions often pose unique problems that may go unrecognised. Here, we identify 14 challenges that infant EEG researchers may encounter when designing new experiments, collecting data, and conducting data analysis. Challenges related to the experimental design are: (1) small sample size and data attrition, and (2) varying arousal in younger infants. Challenges related to data acquisition are: (3) determining the optimal location for reference and ground electrodes, (4) control of impedance when testing with the high-density sponge electrode nets, (5) poor fit of standard EEG caps to the varying infant head shapes, and (6) ensuring a high degree of temporal synchronisation between amplifiers and recording devices during dual-EEG acquisition. Challenges related to the analysis of longitudinal and social neuroscience datasets are: (7) developmental changes in head anatomy, (8) prevalence and diversity of infant myogenic artefacts, (9) a lack of stereotypical topography of eye movements needed for the ICA-based data cleaning, (10) and relatively high inter-individual variability of EEG responses in younger cohorts. Additional challenges for the analysis of dual EEG data are: (11) developmental shifts in canonical EEG rhythms and difficulties in differentiating true inter-personal synchrony from spurious synchrony due to (12) common intrinsic properties of the signal and (13) shared external perturbation. Finally, (14) there is a lack of test-retest reliability studies of infant EEG. We describe each of these challenges and suggest possible solutions. While we focus specifically on the social neuroscience and longitudinal research, many of the issues we raise are relevant for all fields of infant EEG research

14 challenges and their solutions for conducting social neuroscience and longitudinal EEG research with infants.

The use of electroencephalography (EEG) to study infant brain development is a growing trend. In addition to classical longitudinal designs that study the development of neural, cognitive and behavioural functions, new areas of EEG application are emerging, such as novel social neuroscience paradigms using dual infant-adult EEG recordings. However, most of the experimental designs, analysis methods, as well as EEG hardware were originally developed for single-person adult research. When applied to study infant development, adult-based solutions often pose unique problems that may go unrecognised. Here, we identify 14 challenges that infant EEG researchers may encounter when designing new experiments, collecting data, and conducting data analysis. Challenges related to the experimental design are: (1) small sample size and data attrition, and (2) varying arousal in younger infants. Challenges related to data acquisition are: (3) determining the optimal location for reference and ground electrodes, (4) control of impedance when testing with the high-density sponge electrode nets, (5) poor fit of standard EEG caps to the varying infant head shapes, and (6) ensuring a high degree of temporal synchronisation between amplifiers and recording devices during dual-EEG acquisition. Challenges related to the analysis of longitudinal and social neuroscience datasets are: (7) developmental changes in head anatomy, (8) prevalence and diversity of infant myogenic artefacts, (9) a lack of stereotypical topography of eye movements needed for the ICA-based data cleaning, (10) and relatively high inter-individual variability of EEG responses in younger cohorts. Additional challenges for the analysis of dual EEG data are: (11) developmental shifts in canonical EEG rhythms and difficulties in differentiating true inter-personal synchrony from spurious synchrony due to (12) common intrinsic properties of the signal and (13) shared external perturbation. Finally, (14) there is a lack of test-retest reliability studies of infant EEG. We describe each of these challenges and suggest possible solutions. While we focus specifically on the social neuroscience and longitudinal research, many of the issues we raise are relevant for all fields of infant EEG research.This research was funded by an ESRC Transforming Social Sciences collaboration grant (ES/N006461/1) to VL and SW, and by ESRC FRL Fellowship (ES/N017560/1) to SW

Noninvasive brain stimulation techniques can modulate cognitive processing

Recent methods that allow a noninvasive modulation of brain activity are able to modulate human cognitive behavior. Among these methods are transcranial electric stimulation and transcranial magnetic stimulation that both come in multiple variants. A property of both types of brain stimulation is that they modulate brain activity and in turn modulate cognitive behavior. Here, we describe the methods with their assumed neural mechanisms for readers from the economic and social sciences and little prior knowledge of these techniques. Our emphasis is on available protocols and experimental parameters to choose from when designing a study. We also review a selection of recent studies that have successfully applied them in the respective field. We provide short pointers to limitations that need to be considered and refer to the relevant papers where appropriate

Non-invasive Brain Stimulation as a Set of Research Tools in NeuroIS: Opportunities and Methodological Considerations

NeuroIS is a growing field that builds on neuroscience to improve the understanding of human interaction with information technologies and information systems. One can investigate causal relationships between brain activity patterns, cognitive processes, and behavior in a non-invasive way via using non-invasive brain stimulation (NIBS) tools, but researchers in the neuroIS community have yet to do so. We introduce NIBS, show how it can address caveats found in current research, describe the implementation of a NIBS protocol, and assess what these tools can bring to the neuroIS field