Browsing by Author "Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland"

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    Brain basis of sharing and transmitting representations of social world
    (Aalto University, 2018) Smirnov, Dmitry; Nummenmaa, Lauri, Prof., University of Turku, Finland; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Brain and Mind Laboratory; Perustieteiden korkeakoulu; School of Science; Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
    Social communication is a crucial element of human behavior. Every day we resolve conflicts, empathize with our peers, exchange opinions and observe other's behaviors. While the brain basis of these processes has been studied in single individuals, it remains unresolved how such complex patterns of social interaction are parsed in the brains of interacting humans. This thesis addresses the brain basis of social communication in three domains: motor actions, language and emotions. These represent the main channels of human communication, and are tightly linked between each other. We used functional magnetic resonance imaging to collect the data, naturalistic stimulation as an experimental design principle and pseudo-hyperscanning to address the interaction in the experiments. We developed a novel hypeclassification approach, which combined pattern classification with functional realignment of data to investigate the shared neural codes between interacting individuals.In the first study we compared the neural coupling across multiple observers during active simulation versus passive watching of naturalistic boxing match videos by computing the time-varying intersubject phase synchrony of multiple observers' brain activity. We have shown that shared perspective synchronized brain networks involved in action execution and observation. In the second study we adopted a novel hyperclassification approach to investigate shared neural codes between action execution and observation in two individuals. We successfully showed that observed actions can be classified using the model trained on actor's data. The results revealed that action observation and execution share neural information in the brains of two interacting individuals. In the third study we used pseudo-hyperscanning to investigate the neural "coupling" between individuals telling emotional stories, and listeners of these stories. We measured the synchronization of their brain activity time series and revealed that as the experienced emotions became stronger and more similar between speaker and listener, their neural synchronization in attentional, limbic, somatosensory and midline structures increased. The fourth and final study investigated contextual effects on naturalistic speech comprehension. By manipulating context for a narrative, we addressed functional connectivity in the brain of listeners. Results of this study showed increase in functional connectivity in linguistic, attentional and error monitoring brain networks when individuals successfully understood speech in presence of relevant context. These results provide evidence for significant role of intersubject neural synchronization and shared neural codes in social interaction. Such synchronization may provide a window into mind state of another individual and enhance one's ability to understand and predict behavior of others.
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    Cortical dynamics of speech perception in adverse listening conditions
    (Aalto University, 2015) Miettinen, Ismo; Tiitinen, Hannu, Docent, Aalto University, Finland; May, Patrick, Docent, Aalto University, Finland; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Brain and Mind Laboratory; Perustieteiden korkeakoulu; School of Science; Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
    The perception of speech is usually an effortless and reliable process even in highly adverse listening conditions. In addition to external sound sources, the intelligibility of speech can be reduced by degradation of the structure of speech signal itself, for example by digital compression of sound. This kind of distortion may be even more detrimental to speech intelligibility than external distortion, given that the auditory system will not be able to utilize sound source-specific acoustic features, such as spatial location, to separate the distortion from the speech signal. The perceptual consequences of acoustic distortions on speech intelligibility have been extensively studied. However, the cortical mechanisms of speech perception in adverse listening conditions are not well known at present, particularly in situations where the speech signal itself is distorted. The aim of this thesis was to investigate the cortical mechanisms underlying speech perception in conditions where speech is less intelligible due to external distortion or as a result of digital compression. In the studies of this thesis, the intelligibility of speech was varied either by digital compression or addition of stochastic noise. Cortical activity related to the speech stimuli was measured using magnetoencephalography (MEG). The results indicated that degradation of speech sounds by digital compression enhanced the evoked responses originating from the auditory cortex, whereas addition of stochastic noise did not modulate the cortical responses. Furthermore, it was shown that if the distortion was presented continuously in the background, the transient activity of auditory cortex was delayed. On the perceptual level, digital compression reduced the comprehensibility of speech more than additive stochastic noise. In addition, it was also demonstrated that prior knowledge of speech content enhanced the intelligibility of distorted speech substantially, and this perceptual change was associated with an increase in cortical activity within several regions adjacent to auditory cortex. In conclusion, the results of this thesis show that the auditory cortex is very sensitive to the acoustic features of the distortion, while at later processing stages, several cortical areas reflect the intelligibility of speech. These findings suggest that the auditory system rapidly adapts to the variability of the auditory environment, and can efficiently utilize previous knowledge of speech content in deciphering acoustically degraded speech signals.
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    Decoding emotions from brain activity and connectivity patterns
    (Aalto University, 2018) Saarimäki, Heini; Nummenmaa, Lauri, Prof., University of Turku, Finland; Jääskeläinen, Iiro P., Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Brain and Mind Laboratory; Perustieteiden korkeakoulu; School of Science; Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
    Emotions guide both human and animal behavior providing the means for survival in a constantly changing environment. Different emotions seem to be distinct from each other in several aspects, including physiological changes, bodily sensations, facial expressions, and subjective experience. Whether and how such emotion categories exist at the neural level remains however under debate. The goal of this dissertation was to employ pattern classification methods to investigate the neural underpinnings of different emotion states. Specifically, it was hypothesized that if different emotions have distinct neural bases, we should be able to reliably classify them from brain activity and connectivity patterns. Further, it was hypothesized that the classifier confusions presumably reveal which emotions have similar neural substrates. Multiple emotional states were induced in four studies with altogether 109 participants using emotional movies, mental imagery, and narratives while participants' brain activity was measured with functional magnetic resonance imaging (fMRI). Several approaches to the fMRI data analyses were employed: multivariate pattern classification to distinguish voxel activity and functional connectivity patterns underlying different emotions, representational similarity analysis to compare experienced and neural similarity of different emotions, functional connectivity analysis to reveal emotional modulations in brain connectivity, univariate methods such as general linear model (GLM) to visualize the neural substrates of different emotions, and correlation analyses to compare the relationship of different emotions at different emotion-related components. Results from these studies show that specific emotions can be classified from both voxel activity and functional connectivity patterns. Successful pattern classification of voxel activity across the whole brain shows that different emotions have distinct brain activity patterns that generalize across participants and across emotion induction techniques. Further, emotions that subjectively feel more similar also have more similar neural underpinnings. Functional connectivity is modulated by emotional content and shows distinct patterns for different emotions especially within the default mode network (DMN). DMN regions especially in the cortical midline, together with somatomotor, sensory, and subcortical areas, support most emotions. Finally, distinctness of emotions is related at the level of different components, including facial expressions, bodily sensations, emotional evaluations, subjective experiences, and neural substrates. To conclude, emotions have distinct brain activity and connectivity patterns that encompass large extent of the brain. Emotions can thus be viewed as systemic states that, at a given moment, facilitate and constrain other mental functions.
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    Dynamic similarity of brain activity in humans: from single areas to functional networks
    (Aalto University, 2015) Glerean, Enrico; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Brain and Mind Laboratory; Perustieteiden korkeakoulu; School of Science; Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
    What makes us similar and different? The intriguing problem has been studied throughout the centuries by philosophers and scientists and affects the way we live our lives in relationship to the people around us. The brain can process the external world in a similar way across people and even across animal species, but the boundary between similar/different is a dynamic one that changes in space – "where" in the brain we are similar – and in time – "when" brain activity is similar between us. It has been possible to show how localized brain regions show varying levels of intersubject similarity during controlled and naturalistic experiments using functional magnetic resonance imaging. However, the temporal dimension – "when" brain activity is similar between two brains – has remained poorly explored. Furthermore, the brain is a network and the concept of network-level intersubject similarity poses novel challenges especially when considering inter-individual differences both between and within healthy and clinical populations. Here we studied how intersubject similarity of brain activity is modulated in time due to the content of the stimuli or to the psychological perspective that subjects take. These novel problems led to the development of new methods to quantify instantaneous similarity between brains. In addition, moving emphasis from local neuronal activity to distributed network activity, we addressed the challenge of defining the similarity between brain subnetworks to identify their intersubject similarity in relation to behavioural measures. In the first study we used videoclips to induce strong emotions during fMRI scanning and computed how time-varying intersubject correlation of brain activity was modulated by the emotional experience. Feeling similar emotions makes the brains tick in sync. In the second study we introduced novel measures for instantaneous brain similarity for local activity and for dynamic functional connectivity. In the third study we considered how taking different psychological perspectives is reflected in brain activity. Finally, in the fourth study we isolated functional brain networks of high functioning individuals with autism spectrum disorder and healthy controls while watching a feature film, and proposed a method to correlate the autism quotient and the configuration of brain subnetworks. The work presented here reflects recent developments in human non-invasive neuroscience, by stressing the importance of the temporal dimension from local activity dynamics to time-varying networks and the individuality of each brain in relationship to others. Mutual understanding and similarity of behaviour between individuals might be related to similarity of brain function and structure. Although the causality of such relationships might be difficult to disentangle, the current work proposes tools to quantify them.
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    Identifying and modelling context sensitivity in the auditory system of the brain
    (Aalto University, 2018) Westö, Johan; May, Patric, Dr., Lancaster University, UK; Tiitinen, Hannu, Dr., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Brain and Mind Laboratory; Perustieteiden korkeakoulu; School of Science; Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
    While it seems like an effortless task to make sense of sounds, it is in fact a formidable challenge to turn the continuous stream of pressure variations that reach our ears into meaning. This process relies on the ability to detect spectral sequences, but our understanding of how the brain accomplishes this has remained elusive. This thesis seeks to change that by uncovering the neural mechanisms which allow the auditory system to detect sequences in the stream of pressure variations we generally call sounds. The work presented in this thesis utilises two approaches to shed light on how spectral sequences can be detected by the brain’s neural network. Publications 1 and 2 draw on computational modelling of hierarchical neural networks, while Publications 3 and 4 present new computational methods for analysing data from single-cell recordings. The first approach illustrates that synaptic depression, a form of short-term plasticity of connections between neurons, facilitates sequence selectivity in simulated hierarchical neural circuits in a manner consistent with experimental findings. Sequence selectivity emerges for sequences of increasing duration as the hierarchy is traversed, and single-cell and population responses exhibit stimulus-specific adaptation and mismatch responses, respectively. The second approach, in turn, resulted in new models (context models) for describing neural responses to arbitrary stimuli. These models can detect synaptic depression and sequence selectivity in single-cell recordings, two effects that the commonly used spectro-temporal receptive field is unable to capture. The context models were also found to be superior when compared to another commonly used model of neural behaviour, the multi-filter LN model. This observation held for both simulated data and real data from complex neurons in the visual cortex. In conclusion, this thesis 1) highlights the idea that synaptic depression in a hierarchical network might be one of the underlying mechanisms which lets our brain detect sequences, and 2) yields new tools that could be used for investigating sequence selectivity in the brain. These results may therefore be useful for advancing our understanding of how populations of neurons can make sense of sounds.
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    Interpretable artificial neural networks for fMRI data classification
    (Aalto University, 2023) Gotsopoulos, Athanasios; Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Brain & Mind Laboratory; Perustieteiden korkeakoulu; School of Science; Lampinen, Jouko, Prof., Aalto University, Department of Computer Science, Finland
    Functional magnetic resonance imaging (fMRI) technology allows non-invasive measurement of neuronal activity in the human brain with a combination of reasonable temporal and fine spatial resolution. Recently, multivariate methods have attracted attention in fMRI data analysis to study task-related activation patterns. Concurrent research in the field of machine learning has led to the establishment of inherently multivariate computational graphs that facilitate efficient, robust and interpretable classification of fMRI data. Here we studied methods for classification of fMRI data based on neural networks. In particular, we focused on techniques that assess the contribution of different brain regions to the classification result, referred to as "importance maps" and proposed novel neuroscientifically motivated architectures. In the first study, we successfully classified basic emotions from fMRI data, elicited by short movies and mental imagery, generating whole brain importance maps indicating the contribution of individual voxels to the classification result. The second study provided a comparison of importance extraction methods and their reproducibility, applied to both simulated and real data sets, revealing patterns that do not convey significant univariate information. The third study examined the effect of distractors in visual imagery using classification methods and importance map extraction, identifying robust activation patterns related to shape imagery and a visual distractor in object-selective lateral extrastriate cortex at the junction of left occipital, temporal and parietal lobes. The fourth study examined the use of anatomically driven topologies based on spatial information. In particular, the addition of layers motivated by voxel proximity and brain atlases to the model, led to an increase in the classification accuracy and produced smoother and more interpretable importance maps. The purpose of this thesis is to showcase machine learning techniques specifically designed for analyzing neuroscience data. This work aims to motivate further research towards the use of machine learning as a means to gain a better understanding of the human brain.
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    Opioidergic regulation of human affiliative behavior - Evidence from positron emission tomography studies
    (Aalto University, 2019) Karjalainen, Tomi; Nummenmaa, Lauri, Prof., University of Turku, Finland; Tuominen, Lauri, Dr., The Royal’s Institute of Mental Health Research, Canada; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Human Emotion Systems Laboratory; Perustieteiden korkeakoulu; School of Science; Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
    Humans display a remarkable pattern of affiliative behavior. We talk, laugh, play games, and celebrate various events, such as birthdays. Our social networks are also much larger than those of our evolutionary closest cousins, nonhuman primates. Social life is so important for humans that social problems, such as isolation and loneliness, are detrimental to our mental and somatic health. Sociality is indeed one of the basic human needs, similarly as food, water, and safety. Despite the fundamental role of sociality to humans, the neurobiological mechanisms influencing human affiliative behavior are still poorly understood. Animal models of social behavior suggest that endogenous opioid system–a neurotransmitter system modulating pain and pleasure in all mammals–regulates also affiliative behavior. Proposedly, motivation for social interaction partly arises from decreased opioidergic activity in the brain, and various forms of social behavior increase opioidergic processing. This increase results in pleasant affective states and facilitates inter-personal bonding between the interacting individuals. While the results from animal studies are mostly consistent with this model, it is still unknown whether and how the opioid system regulates affiliative behavior of humans. The aim of this Thesis was to characterize the role of the opioid system in human affiliative behavior using neuroimaging with positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). The Thesis focuses on individual differences in self-reports of approach–avoidance behavior and affective responses measured with fMRI. The Thesis also tested if laughing induces release of endogenous opioids. [11C]carfentanil, a selective µ-opioid receptor (MOR) agonist tracer, was used to quantify cerebral MOR availability in all four studies. The first study showed that cerebral MOR availability is positively associated with approach motivation. This finding is in line with data from animal studies, suggesting that baseline opioidergic activity influences how actively humans seek reward. The second study showed that laughing with friends induces endogenous opioid release, consistent with the hypothesis that laughing facilitates interpersonal bonding in humans via opioidergic mechanisms. The third and fourth studies showed that individuals with high MOR availability, particularly in rostral anterior cingulate cortex and insular cortex, have blunted hemodynamic responses to painful and otherwise arousing movie scenes. These findings are consistent with the opioid system's role in regulation of pain and anxiety, suggesting that inter-individual differences in MOR availability may explain why some humans often find themselves highly aroused, while others may be perfectly calm in the same situations. In sum, results of the Thesis support the involvement of opioids in transmitting not only signals related to pain and pleasure but also to sociality in humans.
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    Prior experience shapes speech perception: a behavioural and neuroimaging perspective
    (Aalto University, 2020) Hakonen, Maria; Tiitinen, Hannu, Dr., Aalto University, Finland; May, Patrick, Dr., Lancaster University, UK; Jääskeläinen, Iiro, Prof., Aalto University, Finland; Koskinen, Miika, Dr., University of Helsinki and Aalto University, Finland; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Brain and Mind Laboratory; Perustieteiden korkeakoulu; School of Science; Jääskeläinen, Iiro, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland; Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
    Recent developments in neuroimaging and computational methods have allowed us to measure brain responses to naturalistic stimuli such as words, sentences, and narratives. This has increased our understanding of how the human brain process connected speech. However, it is still unclear how the brain grasps the meaning of acoustically distorted speech. Further, the question of why an interpretation of the same narrative can vary between individuals has remained unknown. This thesis illuminated these issues by studying how prior experience affects speech perception. The first studies of the thesis demonstrated that initially unintelligible, acoustically distorted speech stimuli can be rendered intelligible by presenting them after only a single exposure to their respective intact counterparts. The intelligibility of sentences increased more than that of words, and vowels remained unintelligible. The contrast between the magnetoencephalography response to the distorted vowels presented before versus after the intact vowels revealed enhanced source current density in the auditory cortex and surrounding areas at the latencies of 130–160 ms from stimulus onset. The corresponding contrast between the first and second presentations of distorted sentences in functional magnetic imaging (fMRI) revealed modulations in responses generated in the primary auditory cortex and surrounding areas as well as in several extralinguistic brain areas that have been associated with memory and executive functions in previous studies. The final study showed that when subjects share a cultural family background, and therefore presumably have also accumulated more similar experience throughout their lifetime, this is reflected in the interpretation and neural processing of spoken narratives. In sum, the results of this thesis show that prior experience can dramatically increase speech intelligibility in acoustically adverse conditions, and lexico-semantic information in long-term memory seems to be important in this process. The results provide further evidence that the activity in the auditory areas is not only modulated by auditory information but also by prior experience. Moreover, it seems that experience accumulated throughout the lifetime is reflected in speech processing. The results of this thesis increase knowledge of speech processing in acoustically suboptimal conditions. The results may also help to overcome potential challenges in mutual understanding between individuals from different cultural backgrounds.
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    The quest for consistency: Effects of node definition and preprocessing on the structure of functional brain networks
    (Aalto University, 2018) Korhonen, Onerva; Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland; Tietotekniikan laitos; Department of Computer Science; Complex Systems Research Area; Perustieteiden korkeakoulu; School of Science; Saramäki, Jari, Prof., Aalto University, Department of Computer Science, Finland
    Network neuroscience models the human brain as a collection of structural and functional networks. Brain network studies have broadened our understanding on how the brain works, revealing hubs of information transfer and a modular structure that reflects the balance between functional segregation and integration. Brain network structure depends on age, cognitive task, and health and disease. The most recent research paradigms investigate how brain networks change in time and use multilayer networks to simultaneously capture brain structure and function. However, a number of methodological questions related to network neuroscience remains unanswered. Importantly, the neuroscientific community lacks a standard definition of network nodes, although the properties of networks strongly depend on what their nodes represent. Similarly, the effects of preprocessing methods on brain network structure are not fully known. Regions of Interest (ROIs) are commonly used as nodes of functional brain networks. ROIs are brain areas that contain many measurement voxels in the case of functional magnetic resonance imaging (fMRI) or source vertices in the case of electro- and magnetoencephalography (EEG, MEG). ROIs are defined using anatomy, function, or connectivity. The time series of ROIs are typically obtained by averaging the time series of voxels or vertices of each ROI. The ROI approach is based on the assumption of functional homogeneity: all voxels or vertices of the ROI are assumed to behave similarly. In my Thesis, I explore methodological issues related to brain networks, in particular node definition, functional homogeneity, and preprocessing effects. In the first study, I show that signal mixing and inaccuracies of inverse modelling lead to low levels of functional homogeneity of ROIs in EEG and MEG studies. I introduce an optimized inverse collapse weighting operator as a possible solution. In the second study, I demonstrate that ROIs in three commonly-used fMRI parcellations display low levels of functional homogeneity. Therefore, they should not be used as nodes of functional brain networks without further consideration. In the third study, I investigate the nontrivial effects that a commonly used preprocessing method, spatial smoothing, has on the structure of fMRI brain networks. Finally, in the fourth study, I investigate time-dependent changes in functional homogeneity and local structure of fMRI networks. This leads me to ask if any set of static ROIs can be used to describe brain networks because they are dynamic. My results highlight possible pitfalls of some methods presently used in network neuroscience. Therefore, further careful work is needed to ensure that the methodological basis required for working in the frontier of network neuroscience is indeed solid.
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    Speech Motor System Mediates Phonetic Categorization
    (Aalto University, 2016) Alho, Jussi; Jääskeläinen, Iiro, Senior Scientist, Aalto University, Department of Neuroscience and Biomedical Engineering, Finland; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Brain and Mind Laboratory; Perustieteiden korkeakoulu; School of Science; Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
    Phonemes, the elementary units of speech, greatly vary in their acoustic structure when produced by different speakers in different contexts. The listener's brain therefore faces a fundamental challenge of mapping highly variable acoustic signals to discrete phonetic categories. Whether the speech motor system, primarily engaged in speech production, is involved in solving this perceptual categorization challenge is still under debate. This thesis investigated the influence that the speech motor system exerts on the processing of speech sounds by recording brain activity with magnetoencephalography (MEG). Considering that the perceptual processes leading to the categorization of speech sounds unfold within 200 ms after the sound onset, MEG, as a direct measure of neuronal activity with millisecond temporal accuracy, provides an ideal non-invasive brain imaging method for the purpose of this thesis. Several methodological approaches were used, including equivalent current dipoles and minimum-norm estimates for modeling the cortical activity, stimulus-specific adaptation of evoked responses for inferring neuronal stimulus selectivity, and phase synchrony of neural oscillations for examining connectivity between brain areas. The results show that 1) the auditory information of a speech sound is integrated with the listener's motor information of how the speech sound is articulated within 200 ms after the sound onset, 2) this sensorimotor integration facilitates the categorization of speech sounds, 3) the speech motor system contains categorical representations of speech sounds, and 4) these categorical representations depend on attentive listening to the sounds. Together, these results demonstrate that when humans attend to speech, the speech motor system constrains the acoustic-phonetic interpretation of speech sounds into discrete categories through the integration of auditory and motor information. This thesis therefore expands the knowledge on the role of the speech motor system in phonetic processing and adds evidence to the view that, rather than being a purely sensory process, speech perception is sensorimotor in nature.
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    Usability of emerging technologies - User studies with wearable, multimodal and augmented reality solutions
    (Aalto University, 2018) Aaltonen, Iina; Laarni, Jari, Dr., VTT Technical Research Centre of Finland Ltd, Finland; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Perustieteiden korkeakoulu; School of Science; Sams, Mikko, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
    Working life is undergoing a gradual change from using computers to devices that enable access to information anywhere and anytime. The devices once seen only in science fiction films are permeating our homes and workplaces. In the work context, however, the introduction of new technologies has not always been a painless process for the users despite usability improvement efforts. Nevertheless, working life is now facing an abundance of emerging technologies whose suitability for work is as yet unknown. The six user studies of this thesis examine the usability of emerging technologies and their suitability for work in the context of navigation, maintenance, telerobotics, robotic surgery, and e-justice in courts. Additionally, aspects related to their evaluation are considered. The emerging technologies cover wearable, multimodal and augmented reality solutions. Wearable devices are bodyworn computers or interfaces. Augmented reality means that the user is presented with information that enriches what is seen or experienced in the real world. With multimodal systems, the user is presented with feedback through multiple sensory channels or the user interacts using multiple input modes or devices. A requisite for all of these technologies is well-functioning electronic information exchange. The examined technologies were mostly in the early development stages, meaning that the potential of the technologies for the users in the context of work gained more emphasis than usability evaluations in the traditional sense. The qualitative research methods included questionnaires, interviews, observations, focus groups and future workshops. This thesis offers a collection of practical user aspects that need to be considered when designing, developing and adopting these technologies at workplaces. Most of the evaluated technologies were estimated to be useful for work tasks, although their suitability for work contexts was partially limited. Firstly, the issues of robustness and distractibility were raised especially regarding wearables, although wearables otherwise feel easy and natural to use. Secondly, the redundancy offered by multimodal solutions can benefit users with added certainty, but can also cause confusion in multiple ways. Thirdly, augmented guidance is easy to follow, but its usefulness for experienced workers is unclear. Finally, when technologies bear combinations of these characteristics, issues such as mental load, ergonomics, workflow, collaboration and information presentation need careful consideration. Suitable user evaluation approaches are suggested for these technologies, with a special emphasis on the often under-recognised multimodal interaction. The results will facilitate designing future technologies with the user's best interests in mind, benefiting the users in general, but especially future workers and employers, in addition to researchers developing and evaluating these solutions.