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Objective: Channel selection in electroencephalogram (EEG)-based brain-computer interface (BCI) has been extensively studied for over two decades, with the goal to select optimal subject-specific channels that can enhance the overall decoding efficacy of BCI. With the emergence of deep learning (DL) based BCI models, there arises a need for fresh perspectives and novel techniques to conduct channel selection. In this regard, subject-independent channel selection is relevant, since DL models trained using cross-subject data offer superior performance, and the impact of inherent inter-subject variability of EEG characteristics on subject-independent DL training is not yet fully understood. Approach: Here, we propose a novel methodology for implementing subject-independent channel selection in DL based motor imagery (MI)-BCI, using layer-wise relevance propagation (LRP) and neural network pruning. Experiments were conducted using Deep ConvNet and 62-channel MI data from Korea University (KU) EEG dataset. Main Results: Using our proposed methodology, we achieved a 61% reduction in the number of channels without any significant drop (p=0.09) in subject-independent classification accuracy, due to the selection of highly relevant channels by LRP. LRP relevance based channel selections provide significantly better accuracies compared to conventional weight based selections while using less than 40% of the total number of channels, with differences in accuracies ranging from 5.96% to 1.72%. The performance of the adapted sparse-LRP model using only 16% of the total number of channels is similar to that of the adapted baseline model (p=0.13). Furthermore, the accuracy of the adapted sparse-LRP model using only 35% of the total number of channels exceeded that of the adapted baseline model by 0.53% (p=0.81). Analyses of channels chosen by LRP confirm the neurophysiological plausibility of selection, and emphasize the influence of motor, parietal, and occipital channels in MI-EEG classification. Significance: The proposed method addresses a traditional issue in EEG-BCI decoding, while being relevant and applicable to the latest developments in the field of BCI. We believe that our work brings forth an interesting and important application of model interpretability as a problem-solving technique.

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This paper investigates the Twitter interaction patterns of journals from the Science Citation Index (SCI) of Master Journal List (MJL). A total of 953,253 tweets extracted from 857 journal accounts, were analyzed in this study. Findings indicate that SCI journals interacted more with each other but much less with journals from other citation indices. The network structure of the communication graph resembled a tight crowd network, with Nature journals playing a major part. Information sources such as news portals and scientific organizations were mentioned more in tweets, than academic journal Twitter accounts. Journals with high journal impact factors (JIFs) were found to be prominent hubs in the communication graph. Differences were found between the Twitter usage of SCI journals with Humanities and Social Sciences (HSS) journals.

Altmetrics are new-age research impact metrics that hold the promise of looking beyond the traditional methods of measuring research impact. Altmetrics are real-time metrics that show the outreach of scientific research among an audience from different academic and non-academic backgrounds. Several altmetric systems have been developed in the last few years, either as a cumulative exploratory tool that showcases the different metrics from the various altmetric sources, or as part of existing publisher systems and databases. In the first part of this study, we have analyzed features of nine different altmetric systems, two academic social networking systems, and five other types of systems, including digital libraries, publisher systems, and databases. Results of a feature analysis indicated that the overall coverage of individual features by the systems is moderate, with maximum coverage being 27 out of 33 features analyzed. Features like the visualization of metrics, altmetric sources and bibliometric sources were not found in many systems. Identified gaps were later implemented in the second part of the study, wherein we developed a prototype system, called Altmetrics for Research Impact Actuation (ARIA). We also conducted a user evaluation study of the prototype, the outcome of which was used to improve certain features of ARIA based on user feedback.