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Brain Computer Interfaces (BCIs) consist of an interaction between humans and computers with a specific mean of communication, such as voice, gestures, or even brain signals that are usually recorded by an Electroencephalogram (EEG). To ensure an optimal interaction, the BCI algorithm typically involves the classification of the input signals into predefined task-specific categories. However, a recurrent problem is that the classifier can easily be biased by uncontrolled experimental conditions, namely covariates, that are unbalanced across the categories. This issue led to the current solution of forcing the balance of these covariates across the different categories which is time consuming and drastically decreases the dataset diversity. The purpose of this research is to evaluate the need for this forced balance in BCI experiments involving EEG data. A typical design of neural BCIs involves repeated experimental trials using visual stimuli to trigger the so-called Event-Related Potential (ERP). The classifier is expected to learn spatio-temporal patterns specific to categories rather than patterns related to uncontrolled stimulus properties, such as psycho-linguistic variables (e.g., phoneme number, familiarity, and age of acquisition) and image properties (e.g., contrast, compactness, and homogeneity). The challenges are then to know how biased the decision is, which features affect the classification the most, which part of the signal is impacted, and what is the probability to perform neural categorization per se. To address these problems, this research has two main objectives: (1) modeling and quantifying the covariate effects to identify spatio-temporal regions of the EEG allowing maximal classification performance while minimizing the biasing effect, and (2) evaluating the need to balance the covariates across categories when studying brain mechanisms. To solve the modeling problem, we propose using a linear parametric analysis applied to some observable and commonly studied covariates to them. The biasing effect is quantified by comparing the regions highly influenced by the covariates with the regions of high categorical contrast, i.e., parts of the ERP allowing a reliable classification. The need to balance the stimulus's inner properties across categories is evaluated by assessing the separability between category-related and covariate-related evoked responses. The procedure is applied to a visual priming experiment where the images represent items belonging to living or non-living entities. The observed covariates are the commonly controlled psycho-linguistic variables and some visual features of the images. As a result, we identified that the category of the stimulus mostly affects the late evoked response. The covariates, when not modeled, have a biasing effect on the classification, essentially in the early evoked response. This effect increases with the diversity of the dataset and the complexity of the algorithm used. As the effects of both psycho-linguistic variables and image features appear outside of the spatio-temporal regions of significant categorical contrast, the proper selection of the region of interest makes the classification reliable. Having proved that the covariate effects can be separated from the categorical effect, our framework can be further used to isolate the category-dependent evoked response from the rest of the EEG to study neural processes involved when seeing living vs. non-living entities.

Dataset of a priming visual experiment acquired in the scope ofARC-1923 UMONS3 program. This dataset is linked to the publication "La Fisca et al.,Revealing Biases in BCI Experiments: Can Uncontrolled Covariates Fool You, 2022. Please refer to the publication for citation.

This paper introduces action research as an approach for change and development in management research based on its outstanding characteristics and discusses the application notion of action research by critically reviewing its strengths and limitations as well as representing an example case to demonstrate how to employ it in practical setting. Finally, the author draws attention to bias avoidance in action research by introducing three effective practices to mitigate against sources of bias.

This chapter explores the linkages between gendered public administration and structural inequalities in academia. Changing demographics highlight the importance of family-friendly policies in the workplace. According to the U.S. Bureau of Labor Statistics (2009), labor force participation rates of mother (working full-time and part-time) with children under age 18 increased from 47% in 1975 to 71% in 2007. In comparison, when the age of a child is considered, the labor force participation rates decrease to 64% for mothers with children younger than 6 years of age. The number of women in graduate programs has also increased in recent years. The U.S. Digest of Education Studies reported that in 2007, 63% of graduate students were women (Snyder, 2009). Similarly, the National Association of Schools of Public Affairs and Administration (2008) reported that among the 159 respondents of the 268 Administration institutional members, women represented 53% of the doctoral recipients in the fall semester of 2007.