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Classification of finger movements through optimal EEG channel and feature selection
No full textIntroduction: Electrencephalography (EEG)-based brain-computer interfaces (BCIs) have become popular as EEG is accepted as the simplest and non-invasive neuroimaging modality to record the brain's electrical activity. In the current BCI research context, apart from predicting extremity movements, recent BCI studies have been interested in accurately predicting finger movements of the same hand using different pattern recognition methods over EEG data collected based on motor imagery (MI), through which a mental image of the desired action is generated when a person ideally simulates or imagines carrying out a certain motor task. Although several pattern recognition methods have already been recommended in literature, majority of the studies focusing on classifying five finger movements, were based on study designs that neglected or excluded the idle state of brain (i.e., no mental task state) during which brain does not carry out any MI task. This study design may result in an increasing number of false positives and a significant decrease in the prediction rates and classification performance. Moreover, recent studies have focused on improving prediction performance using complex feature extraction and machine learning algorithms while ignoring comprehensive EEG channels and feature investigation in the prediction of finger movements from EEGs. Therefore, the objectives of this study are threefold: (i) to develop a more viable and practical system to predict the movements of five fingers and the no mental task (NoMT) state from EEG signals (ii) to analyze the effects of the statistical-significance based feature selection method over four different feature domains (nonlinear domain, time-domain, frequency-domain and time-frequency domain) and their combinations, and (iii) to test these feature sets with different and prominent classifiers.Methods: In this study, our major goal is not to explore the best machine algorithm performance, but to investigate the best EEG channels and features that can be used in the classification of finger movements. Hence, the comprehensive analysis of the effectiveness of EEG channels and features is performed utilizing a statistically significant feature distribution over 19 EEG channels for each feature set independently. A bulky dataset of electroencephalographic MI for EEG-based BCIs is used in this study. A total of 1102 EEG features supplied from different feature domains have been investigated. Subsequently, these features were tested with eight well-known classifiers, comprising Decision tree, Discriminant analysis, Naive Bayes, Support vector machine, k-nearest neighbor, Ensemble learning, Neural networks, and Kernel approximation.Results: For subject-dependent analysis, the maximum accuracy of 59.17% was obtained using the EEG features that were selected the most (including (i) energy and variance of five frequency bands in frequency-domain feature set, (ii) all feature types in time-domain, time-frequency domain, and nonlinear domain feature sets) and all EEG channels by the Support vector machine algorithm. For subject-independent analysis, the maximum accuracy of 39.30% was obtained using the mostly selected EEG features (which are (i) all feature types excluding the waveform length, average amplitude change value, absolute difference in standard deviation, and slope-change value feature types in time-domain feature set, (ii) the energy and variance values of all frequency bands except gamma frequency band in frequency-domain feature set, (iii) the entropy value of five frequency bands in time-frequency-domain feature set, and (iv) SD2 and SD1/SD2 values where lag = 1 in nonlinear feature set) and EEG channels (which are (i) some definite EEG channels including 2nd, 3rd, 7th, 11th, 13th, 14th, and 15th channels in time-frequency-domain feature set and (ii) all EEG channels in time-domain, frequency-domain, and nonlinear feature sets) by the Support vector machine classifier.Discussion: Experimental results demonstrate that despite the high-class number, the proposed approach obtained a modest yet considerable advancement in finger movement prediction when the results are compared to the results of similar studies. Additionally, for almost all feature sets, the statistical significance-based feature reduction method improves the prediction performance in the most of classifiers, contributing elaborate EEG channel and feature analysis. Nonetheless, in this study, we used an EEG dataset recorded fr</p
Physiotherapists' preparedness for a national hip screening program in children with cerebral palsy: a survey study
No full textObjective: Cerebral palsy (CP) is a prevalent neurological disorder a!ecting 2-3 per 1000 live births globally. A common orthopedic consequence in CP is hip dislocation, with an incidence ranging from 15% to 30%. This study aimed to assess physiotherapists' (PTs) awareness and knowledge of hip dislocation in CP and identify knowledge gaps in this field. Methods: A cross-sectional survey was administered to PT working in healthcare institutions across Türkiye. The online anonymous survey, hosted on Google Forms, received responses from 128 PTs. It included questions related to the diagnosis, treatment approaches, and clinical experience concerning hip dislocation/subluxation. The survey consisted of 3 types of questions: demographic questions, yes/no knowledge questions, and opinion-suggestion questions. Results: The majority of PTs participating in this study work with pediatric patients. Physiotherapists had basic knowledge about hip dislocation although we observed significant gaps in areas related to routine screening programs and advanced treatment modalities. PTs incorrectly answered 3 out of 8 knowledge-based questions. The majority emphasized the importance of early diagnosis and treatment and expressed a need for more education and awareness programs. Conclusion: Physiotherapist provided important opinion-suggestion insights. The findings indicate a need for improved education and training for PT concerning hip dislocation in children with CP. We believe that the most appropriate screening method for Türkiye is to establish a screening program with a multidisciplinary structure formed between pediatric orthopedists, physical medicine and rehabilitation specialists (PM&R), PT, and family physicians (FPs). Level of Evidence: N/A
Factors Affecting Social Stigma and Health-Seeking Behavior in COVID-19 Survivors: A Field Study in the West of Turkey
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The relationship between nursing students’ critical thinking disposition and their problem-solving thoughts
No full textThe purpose of study is to examine relationship between nursingstudents’ critical thinking dispositions and their thoughts about problemsolving. The universe of descriptive and correlational type of researchconsisted of 520 students studying in the nursing department of auniversity. The study did not involve sample selection, 361 students (%69)who volunteered to participate in study, met inclusion criteria wereincluded in study. Data were collected using socio-demographiccharacteristics form, Critical Thinking Disposition Scale, Problem SolvingThoughts Scale. Mann Whitney U test, Kruskal Wallis test, and Spearmancorrelation analysis were used in the analysis of the data. The mean scoreof students Critical Thinking Disposition is 193.50±30.29 and the meanscore of Problem-Solving Thoughts is 99.13±12.76. A significant differencewas found between the grades in which students studied and mean scoresof Critical Thinking Disposition and its sub-dimensions (p=0.00). Asignificant difference was found between students’ income status,university-weighted general grade point averages, article reading status,and Critical Thinking Disposition and Systematicity, Flexibility subdimensions mean scores (p<0.05). A significant difference was foundbetween students’ gender and mean scores of Execution and Evaluationsub-dimensions of Problem-Solving Thoughts Scale (p=0.00, p=0.02). Asignificant difference was found between students grades and mean scoresof Problem-Solving Thoughts and Planning and Execution subdimensions (p=0.00). A strong positive relationship (r: 0.66; p= 0.00) wasfound between students’ Critical Thinking Disposition and their Thoughtson Problem-Solving. It was found that the students participating in thestudy had above-average scores on both the Critical Thinking DispositionScale and the Scale of Opinions on Problem Solving, and that there was astrong correlation between their critical thinking dispositions and theiropinions on problem-solving. </p
The Relationship Between Attachment Style and Aggression and Addiction Severity in People with Substance Use Disorders
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