Context: Motor Imagery (MI) signal classification is a crucial task for developing Brain-Computer Interfaces (BCIs) that allow people to control devices using their thoughts. However, traditional machine learning approaches often suffer from limited performance due to inter-subject variability and limited data availability. In response, adversarial learning has emerged as a promising solution to enhance the resilience and accuracy of BCI systems. However, to the best of our knowledge, there has not been a review of the literature on adversarial learning specifically focusing on MI classification. Objective: The objective of this thesis is to perform a Systematic Literature Review (SLR) focusing on the latest techniques of adversarial learning used to classify motor imagery signals. It aims to analyze the publication trends of the reviewed studies, investigate their use-cases, and identify the challenges in the field. Additionally, this research recognizes the datasets used in previous studies and their associated use-cases. It also identifies the pre-processing and adversarial learning techniques, and compare their performance. Additionally, it could aid in evaluating the replicability of the studies included. The outcomes of this study will assist future researchers in selecting appropriate datasets, pre-processing, and adversarial learning techniques to advance their research objectives. The comparison of models will also provide practical insights, enabling researchers to make informed decisions when designing models for motor imagery classification. Furthermore, assessing reproducibility might help in validating the research outcomes and hence elevate the overall quality of future research. Method: A thorough and systematic search following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines is undertaken to gather primary research articles from several databases such as Scopus, Web of Science, IEEEXplore, PubMed, and ScienceDirect. Two independent reviewers evaluated the articles obtained based on predetermined eligibility criteria at the title-abstract level, and their agreement was measured using Cohen's Kappa. The articles that fulfill the criteria are then scrutinized at the full-text level by the same reviewers. Any discrepancies are resolved by the judge – played by the supervisor. Critical appraisal was employed to choose appropriate studies for data extraction, which was subsequently examined using bibliometric and descriptive analyses to answer the research questions. Result: The study's findings indicate substantial growth within the domain over the past six years, notably propelled by contributions from the Asian region. However, the need for augmented collaboration becomes evident as evidenced by the prevalence of insular co-author networks. Four principal use-cases for adversarial learning are identified, spanning data augmentation, domain adaptation, feature extraction, and artifact removal. The favored datasets are BCI Competition IV's 2a and 2b, often accompanied by band-pass filtering and exponential moving standardization preprocessing. This study identifies two primary adversarial learning techniques: GAN and Adversarial Training. GAN is mainly used for data augmentation and artifact removal, while adversarial training is employed for domain adaptation and feature extraction. Based on the results reported in the chosen papers, the accuracy achieved for data augmentation and domain adaptation use cases is nearly identical at 95.3%, while the highest accuracy for the feature extraction use case is 86.91%. However, for artifact removal, both correlation and root mean square methods have been referenced. Furthermore, a reproducibility table has been established which may help in evaluating the replicability of the selected studies . Conclusion: The outcomes provide researchers with valuable perspectives on less-explored areas that hold room for additional enhancement. Ultimately, these perspectives hold the promise of improving the practical applications intended to support individuals dealing with motor impairments.

While cutting-edge research has shown how – from a neural and cognitive point of view – human beings are perceived and elaborated differently from objects, in social psychology different studies demonstrated that this human-object divide fades in several circumstances. Research in social psychology is continuing to advance the knowledge on dehumanization and objectification phenomenon in which human beings are perceived and elaborated more similar to an object and less like a human being. Recently, this has been demonstrated quite literally directly comparing human stimuli with a mind and perceptually similar mindless objects (Vaes et al., 2019, 2020). Such direct comparisons allow us to demonstrate how the well-documented human-object divide tends to fade during dehumanization and objectification phenomena. Presenting five research studies, this thesis aims not only at proving how de-mentalized human stimuli are cognitively perceived as object-like (Chapter 2 and 3), but also at showing how these phenomena are influencing more subtle, un-controlled behaviour processes that impact human social interactions (Chapter 4). Specifically, in Chapter 2, two similar EEG studies aimed at exploring the timeline of the mentalization process by adapting a paradigm in which the human-object divide is investigated. By manipulating both perceptual and contextual information, ingroup and outgroup human faces together with their identity-matched doll-like avatar faces were presented while registering participants’ neural correlates. Thanks to the direct comparison between mindless and mindful targets our goal was to unravel the time course of mentalization and its underlying processes. By adapting the same paradigm, in Chapter 3 we explored the process of sexual objectification and presented sexually objectified men and women with their gender-matched doll-like avatars. Our primary goal was to investigate how objectified men and women are perceptually and cognitive perceived by looking at a sample of gay men. By directly comparing mindless and mindful targets we wanted to understand whether sexual objectification might be target (i.e., always mainly directed towards women regardless of the perceivers sexual orientation) or agent specific (i.e., directed towards different targets depending on the perceivers sexual orientation). Moreover, we also wanted to explore what might drive heterosexual men and women and gay men to objectify others. Finally, the purpose of Chapter 4 was to investigate an implicit and unconscious consequence of sexual objectification. By presenting objectified and non-objectified women expressing happiness and anger we measured participants’ spontaneous mimicry responses. Our goal was to determine whether sexual objectification – a phenomenon in which women are considered as object-like – might influence such an uncontrolled and implicit human behaviour that affects normal social interactions.

Extensive research has been conducted on the relationship between brain activity and personality traits, and several theories propose a lateralization of specific personality qualities. A prominent model suggests frontal lateralization of motivational direction, specifically, the behavioral inhibition and activation systems (BIS/BAS), with greater right frontal activity linked to behavioral inhibition and greater left frontal activity linked to behavioral activation. Recent studies have presented contrasting findings in the absence of this correlation. With the present study I aimed to investigate the link between frontal lateralization and the BIS/BAS. I further examined the test-retest reliability of resting-state frontal alpha asymmetry (FAA), and of the BIS/BAS scale. Resting-state frontal EEG asymmetry and participants’ responses to the BIS/BAS scale were collected from University of Skövde students on multiple occasions. FAA were obtained from electrode sites F4-F3, F6-F5, and F8-F7 over three sessions, two weeks apart, along with BIS/BAS scores from the first and third sessions. Within-subject FAA showed variability over time, suggesting FAA to be a less reliable measure of personality traits. Only two out of the four BIS/BAS subscales demonstrated consistent scores, raising doubts about the reliability of using it to assess personality traits. BAS Drive correlated negatively with FAA, contrary to the expected direction, but no other significant correlation was observed between resting-state FAA and BIS/BAS. Verifying FAA as an indicator of BIS and BAS is important for drawing meaningful associations between them. Future research should consider employing a repeated measures design and a larger sample size to enhance the understanding of this relationship.

As an able-bodied human, understanding what someone says during a phone call with a lot of background noise is usually a task that is quite easy for us as we are aware of what the information is we want to hear, e.g. the voice of the person we are talking to, and the information that is noise, e.g. music or ambient noise in the background. While dealing with noise of all kinds for most humans proves to be the easiest, it is a very hard task for algorithms to deal with noisy data. Unfortunately for some beneficial and interesting applications, like Brain Computer Interfaces (short BCI) based on Electroencephalography (short EEG) data, noise is a very prevalent problem that greatly hinders the progress of making BCIs for real-life applications. In this thesis, we investigate what effect noise added to EEG data has on the classification accuracy of one Spiking Neural Network and one Convolutional Neural Network based classifier for a motor imagery classification task. The thesis shows that already relatively small amounts of noise (10% of original data) can have strong effects on the classification accuracy of the chosen classifiers. It also provides evidence that SNN based models have a more stable classification accuracy for low amounts of noise. Still, their classification accuracy after that declines more rapidly, while CNN based classifiers show a more linear decline in classification accuracy / Att förstå vad någon säger under ett telefonsamtal med mycket bakgrundsljud är en relativt enkel uppgift för en människa eftersom vi är duktiga på att urskilja vilken del av ljudet som är relevant, t.ex. rösten hos den vi pratar med, och vilken del av ljudet som är bakgrundsbrus, t.ex. musik eller omgivningsljud. Även om det är en enkel uppgift för en människa att filtrera bort olika sorters brus så är det betydligt svårare för en algoritm att hantera brusig data. Tyvärr finns det flertalet användbara och intressanta applikationsområden där svårigheten med brus orsakar betydande problem. Ett sådant exempel är braincomputer interfaces (BCI) baserade på elektroencefalografi (EEG) där brus är ett så pass utbrett problem att det begränsar möjligheten att använda BCI i verkliga tillämpningar. I detta examensarbete undersöks hur tillägget av brus till EEG-data påverkar noggrannheten på klassificeringen av hjärnaktivitet vid visualisering av olika rörelser. För detta ändamål jämfördes två typer av klassificerare: ett spiking neural network (SNN) och ett convolutional neural network (CNN). Examensarbetet visar att redan relativt små tillägg av brus (10%) kan ha stor påverkan på klassificeringens noggrannhet. Studien påvisar även att SNN-baserade modeller har en mer stabil noggrannhet för låga mängder brus, men att noggrannheten försämras snabbare vid ökad mängd brus än för CNN-baserade klassificerare som visar en mer linjär försämring.

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