92 research outputs found
SEMANTIC LONG-TERM MEMORY: ASSESSMENT AND EFFECTS OF STEREOSCOPIC 3D EDUCATIONAL CONTENTS USING EEG SIGNALS
No full textEvaluation of Stereoscopic 3D Based Educational Contents for Long-Term Memorization
No full textThe aim of this study is to compare stereoscopic 3D basedand 2D based educational contents for long-term memoriza-tion using electroencephalogram (EEG) signals.Sixty eight healthy young adults were equally dividedinto 2D group and stereoscopic 3D (S3D) group in such away that their fluid intelligence and age were controlledbetween groups. S3D and 2D groups were exposed to S3Dbased and 2D based educational contents, respectively, for learning complex human anatomy concepts. The durationof contents was 30 minutes long and exactly after twomonths of retention they were asked to take a memoryrecall test of twenty multiple choice questions. The EEGsignals were recorded during the recall session.The behavioral responses of both groups were not signifi-cant (p>0.05). However, the EEG source analysis revealedsignificant differences between 2D and S3D groups forlong-term memorization (p<0.05) in the BA 7, BA 10, BA11, and BA 25. The recall of S3D contents involved wide-spread brain neuronal network as compared to 2D contents.In conclusion, human brain processed the S3D contentsdifferently for memorization than 2D contents. The differ-ences may be due to depth perception in S3D contents
Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques
Get PDFBackgroundPresentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.MethodsEEG single trials are decomposed with discrete wavelet transform (DWT) up to the level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.ResultsThe proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60, sensitivities 93.55, specificities 94.85, precisions 92.50, and area under the curve (AUC) 0.93 using SVM and k-NN machine learning classifiers.ConclusionThe proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds
Career Change Intentions of Science and Engineering Undergraduates: A Behavioral Neuroscience Inquiry
Get PDFBased on self-determination theory, this study examined the mental and emotional states of final year Science and Engineering undergraduates in career change considerations. While prior work has explored students’ career choice, career change intentions particularly among the new entrants in the contemporary workforce, remains an under researched domain. A purposive sample of thirty final-year Science and Engineering undergraduates participated in a laboratory experiment in which an electroencephalogram (EEG) device was used to detect their mental states when responding to career decision stimuli involving salary and work-life balance considerations for Research and Development Specialist (scientific career) and Management Associate (non-scientific) positions. The brainwave results showed significantly higher beta activation (which indicated stress levels) for participants who were less self-determined, at the point of reviewing the job description (which included work-life balance company policies) for the Research and Development Specialist (R&D) position. The manipulation of salary levels did not have a significant impact on their stress levels. While almost all participants opted for a R&D career during the experiment, the survey data revealed a stronger intention among less self-determined individuals to leave science at a later juncture due to perceived lower salary and a lack of work-life balance. Overall, this study underscores the importance of self-determination to promote sustainable scientific career. From these findings, implications towards career counselling for contemporary science and engineering workforce are further discussed
Dataset for 'Introducing memory grading scale for semantic long-term retention using resting state EEG functional connectivity'
No full textSemantic long-term memory (LTM) store and
retain concepts, facts and knowledge of external world, which we have learned. This dataset consists of 68 healthy participants resting states EEG data recorded with 128 channels EGI machine with 250 samples per second and re-referenced using REST techniques, along with a supporting file of responses recorded during long-term memory tests (Recall 1 after 30 minutes retention, Recall 2 after 2 months renteion, Recall 3 after 4 months retention, Recall 4 after 6 months retention) and fluid intelligence (Ravan's Advanced Progressive Matrices) test. In addition, participants' age is recorded in years. </p
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