Open Access

Objective: Common spatial pattern (CSP) is a common filter technique used for pre-processing of electroencephalography (EEG) signals for imaginary movement classification tasks. It is crucial to reduce the amount of features especially in cases where few data is available. Therefore, different approaches to reduce the amount of electrodes used for CSP calculation are tried in this research. Methods: Freely available EEG datasets are used for the evaluation. To evaluate the approaches a simple classification pipeline consisting mainly of the CSP calculation and linear discriminant analysis for classification is used. A baseline over all electrodes is calculated and compared against the results of the approaches. Results: The most promising approach is to use the ability of CSP to provide information about the origin of the created filter. An algorithm that extracts the important electrodes from the CSP utilizing these information is proposed. The results show that using subject specific electrode positions has a positive impact on accuracy for the classification task. Further, it is shown that good performing electrode combinations in one session are not necessarily good performing electrodes in another session of the same subject. In addition to the combinations calculated using the developed algorithm, 26 additional electrode combinations are proposed. These can be taken into account when selecting well-performing electrode combinations. In this research we could achieve an accuracy improvement of over 10%. Conclusions: Carefully selecting the correct electrode combination can improve accuracy for classifying an imaginary movement task.

Although the management of customer relationships has become a key priority in firms in a broad range of industries, its effective implementation often creates major challenges. Couched in configuration theory and using a configurational approach, this article investigates how factors associated with firms’ customer management, market approach, and business environment interact and fall into patterns to predict profitability. The results of a fuzzy-set qualitative comparative analysis reveal different configurations to a profitable implementation of customer relationship management. Insight into these configurations contributes to a deeper understanding of digital ecodynamics and advances the understanding of the interplay among important strategic, technological, and environmental factors. The findings of this inquiry can guide managers in designing effective customer relationship management approaches. (Abstract vom Preprint - siehe Bemerkungen - übernommen gemäß CC BY 4.0: http://creativecommons.org/licenses/by/4.0/)