Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data.

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.

Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis.

An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain-computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes' motor images, namely Dataset 2a of BCI Competition IV.

Media and interpersonal channels uses and preferences during the COVID-19 pandemic: the case of the United States, Thailand, and Croatia.

According to media systems dependency theory, increased dependence on media to meet individual needs is directly proportional to greater perceived media importance in one's life and subsequently stronger media effects on one's attitudes and behavior. This dependency relationship intensifies during times of uncertainty or crisis. Although several recent studies have focused on media dependence during health crises such as SARS and H1N1 influenza, insights from eastern countries may not be validated in other hemisphere. Therefore, the purpose of the current study was to adopt a cross-cultural lens to examine how participants from three different continents used media during the COVID-19 pandemic. In total, 860 adults completed a survey during April and May 2020. Participants were asked to provide demographic and socioeconomic details, followed by questions measuring their media consumption, channel preferences, motivations, and perception of their well-being during the COVID-19 pandemic. Results revealed that national culture had a strong influence on media use and preferences during the COVID-19 crisis. For example, the US participants spent the most time using the media but scored the lowest on well-being. This aligns with the claim that in individualistic countries people rely on media more than on their social network. US participants also used the media for surveillance goals more than their Thai or Croatian counterparts. Another cultural difference was that Thai participants reported using the media to relax more than other nationalities, which reflects the Thai's "way of life" and can explain their higher score on well-being. Finally, there were cultural differences in the use of social media. While Croatian and Thai participants' use of social media reflected collectivistic tendencies (using social media for social interaction), among Americans, social media use reflects individualistic trends (using social media for surveillance). The study discusses limitations and suggestions for future research.

Baby Boomers' use of Facebook and Instagram: uses and gratifications theory and contextual age indicators.

Social media research tends to prioritize how young adults - and college students, in particular - use social network sites. While several studies have focused on how Facebook can help alleviate loneliness among older adults, the motives for using other social media platforms, including Instagram, have not been adequately explored. This study therefore focuses on how a uses and gratifications framework applies to older users of Facebook and Instagram, including the relationship to contextual age. A survey of 414 Baby Boomers and Traditionalists was conducted in the Fall of 2019. The results revealed that older adults rely on Facebook and Instagram to compensate for the lack of social activity and face-to-face interactions in their daily lives. These patterns are consistent with social compensation hypothesis and contradict the findings of studies done with college students. In addition, the older adults' life satisfaction was a negative predictor of using these sites for companionship and diversion. Future research should furthermore explore how other personality traits and social situations might influence older individuals use of social media. This knowledge can be particularly useful in times of health pandemic, such as COVID-19, when so many older individuals are confined to their homes and rely on social media for interaction and entertainment. Insight into intergenerational social media usage differences can also benefit advertisers, policy makers, recreational groups, healthcare and social services.

Predictors of Problematic Social Media Use: Personality and Life-Position Indicators.

Given the preponderance of social media in our increasingly saturated media environments, there is a need for greater understanding of how personality traits and states can influence problematic social media use. This study examines whether contextual age indicators (life satisfaction, interpersonal interaction, social activity), the fear of missing out, and the Big Five personality traits are significant predictors of Facebook, Instagram, and Snapchat addictions. A survey of 337 college students reveals that greater social activity is a positive predictor of addiction to Snapchat. Another significant finding is a positive relationship between social media addiction and the fear of missing out, which explained the most variance in addiction scores for Snapchat (16%). An inductive analysis of open-ended responses indicated strong similarities between those who claimed that they were addicted to these social media apps and those said that they were not addicted. Both groups described largely similar usage patterns and media dependency, yet several users did not self-identify as addicted to social media. These patterns indicate that more research is needed to determine how social media users differentiate between normative, heavy, and addicted usage.

Potential risk of acute hepatotoxicity of kodo poisoning due to exposure to cyclopiazonic acid.

Kodo millet (Paspalum scrobiculatum L.) is a staple food of some sections of people of North India. Consumption of Kodo millet is often found to cause intoxication and poisoning. The grains are frequently infested with Aspergillus tamarii Kita, which produced substantial amount of a mycotoxin, cyclopiazonic acid (CPA). Investigations were carried out to evaluate the hepatotoxic/preneoplastic changes in rat liver following single and multiple dose administration of CPA. Results showed a marked increase in the activity of glutamate pyruvate transaminase (GPT) and glutamate oxaloacetate transaminase (GOT) following CPA exposures, suggesting acute hepatotoxicity. Significant increase was also observed in gamma glutamyl transpeptidase (GGT) activity following CPA exposures, indicating preneoplastic changes in the liver. The results reveal that Kodo poisoning might cause acute hepatotoxicity in men and animals. The findings thus suggest that the consumption of contaminated Kodo millet is a serious health hazard due to exposure to CPA produced by Aspergillus tamarii associated with the millet.

Protective effect of tenuazonic acid against dimethyl benz(a)antracene-induced skin carcinogenesis in mice.

Anticarcinogenic potential of tenuazonic acid (TA), a mycotoxin isolated from a fungus, Alternaria alternata, on skin tumorigenesis induced by 7,12-dimethyl benz(a) antracene (DMBA) was investigated. Female Swiss albino mice were exposed topically to 100 nmole of DMBA twice weekly for 20 weeks. Another group of animals was treated with 250 microg TA in acetone daily for a period of 1 week, followed by the same dose of TA prior to every application of DMBA. At the end of 14 weeks, all the animals in the group that was exposed to DMBA alone developed tumors, while 40% of the animals in TA-treated group were found to be tumor free. After 20 weeks, there was no further increase in the number of tumor-bearing animals. Results indicated that prior application of TA significantly delayed the onset of tumorigenesis and also reduced the cumulative number of tumors per tumor-bearing animals. The present studies reveal the antitumor and protective potential of TA against polycyclic aromatic hydrocarbon induced skin carcinogenesis.