Impact assessment of mental subliminal activities on the human brain through neuro feedback analysis
01 Mar 2017-pp 1-6
TL;DR: This work explores the effects of sleep, attention and music on the human brain through analysis of the correlation between specific EEG patterns and the aforementioned activities.
Abstract: Neuro feedback is a type of biofeedback phenomena that shows the activity on the human brain through Electroencephalogram (EEG). EEG measure the electrical activity of the human brain by electrodes placed on different parts of the brain cortex. Since the human brain has complex circular firing wave patterns, EEG allows us to non-invasively measure the electrical activity of the brain waves. The intensities of theses human brain waves vary from individual to individual and changes due to various physiological, mental and physical state of the human body. This work explores the effects of sleep, attention and music on the human brain. Analysis of these activities is used to show the correlation between specific EEG patterns and the aforementioned activities.
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12 Apr 2017
TL;DR: The workflow of making inventory, selecting the theses for digitization, unbinding, scanning, OCRing, formatting, quality checking, converting to secured PDF-A format, binding of theses, uploading into IR(Institutional Repository) and Shodhganga is skilfully explored.
Abstract: A steep escalation is observed in Electronic Thesis and Dissertations??? (ETDs) Repositories throughout the world during the last few years. This increase in ETDs is transforming the way of learning, research and scholarly communication in academic institutions and their scholars. ETDs underpins research and national development, hence, there was a need to setup an ETD at our institutional level. The transformation from print to electronic theses and dissertations has brought new attention to the researchers. Theses and dissertations are considered original research works in every university. The quality of research is also a key criterion while measuring the repute of any academic institution. This paper focuses on the practical problems faced during implementation of this project. We faced many challenges to implement this project but we took it as an opportunity. It highlights techniques to seek knowledge using various software packages. The authors highlight the experiences in developing ETDs and experiments with knowledge discovery software packages. Furthermore, the paper explores the extent to which academic libraries are grappling with the emerging genres of ETDs, for example, the use of linked data to enhance discoverability. The paper also make recommendations to implement the ETDs to enhance effective utilization of ETDs and knowledge discovery. This paper skilfully explores the workflow of making inventory, selecting the theses for digitization, unbinding, scanning, OCRing, formatting, quality checking, converting to secured PDF-A format, binding of theses, uploading into IR(Institutional Repository) and Shodhganga. The success of this innovative project has helped in making the mandatory submission of the Ph.D. thesis possible to the Vivekananda Library at MDU, Rohtak in desired PDF or MS-Word format to protect this important research treasure of the University.
6 citations
TL;DR: The improved signal has been considered based on the generated brain signal in various aspects like human intelligence, memory and also the capability of better feelings.
Abstract: Human brain signals obtained by the human brain sensor electrodes measure the cerebral activities on the human brain. The main aim of our research is to improve the human brain activities based on the human brain signal. The entire procedure contains three steps. The first step is to acquire the brain signal, then develop this brain signal with the proposed method and finally improve the human brain activities with this modified brain signal. The entire procedure will proceed in a proposed Neuroheadset device embedded with necessary sensors using the non-invasive technique. This device will help to acquire the brain signal, modify this signal and improve the brain activities with this modified brain signal. In this research, we illustrated the first two steps like signal acquisition and signal modification. In the experiment, we used Electroencephalogram as an efficient non-invasive signal acquisition technique for acquiring the brain signal and also introduced a proposed method to modify this signal. This method helped to improve the human brain signal using the required times of the iteration process. In the experiment level, several iteration processes have been done to get above 90% improvement rate of the brainwaves. In this research, the improved signal has been considered based on the generated brain signal in various aspects like human intelligence, memory and also the capability of better feelings.
3 citations
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TL;DR: A systematic review of the literature in the field of affective computing in the context of music therapy is presented in this paper, where the authors assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI).
Abstract: Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.
2 citations
01 Feb 2019
TL;DR: A comprehensive critical review of human brain sensor activities related prior researches is demonstrated for constituting an efficient method to improve the performance of maneuverability, visualization, subliminal activities and so forth on human brain activities.
Abstract: The main purpose of this research is to investigate the human brain sensor activities related prior researches towards the needs of an efficient method to improve the human brain sensor activities. Human brain activities mainly measured by brain signal acquired from the brain sensor electrodes positioned on several parts of the brain cortex. Although previous researches investigated human brain activities in various aspects, the improvement of the human brain sensor activities is still unsolved. In today’s world, it is very crucial need for improving the sensor activities of the human brain using that human brain improved signal externally. This research demonstrated a comprehensive critical analysis of human brain activities related prior researches to claim for an efficient method integrated with proposed neuroheadset device. This research presented a comprehensive review in various aspects like previous methods, existing frameworks analysis and existing results analysis with the discussion to establish an efficient method for acquiring human brain signal, improving the acquired signal and developing the sensor activities of the human brain using that human brain improved signal. Demonstrated critical review has expected for constituting an efficient method to improve the performance of maneuverability, visualization, subliminal activities and so forth on human brain activities.
1 citations
25 Mar 2021
TL;DR: This project aims at designing and implementing a device that can be used to detect and monitor the attention and meditation values of a person in real time and sends the control command over wireless to a remote controller.
Abstract: Epileptic seizures are explained as the abnormal electrical activity occurring in the brain due to an internal or external triggering factors. EEG (Electroencephalograph) is used to record brain activity and can be used to detect the seizures before, during or after they occur. These signal characteristics, however differ from patient to patient due to the different emotional and physical wellbeing of the various individuals. In normal circumstances, anti-epileptic medication is used to treat patients but very few systems have been developed to manage and track the seizures. In most extreme and rare cases, some patients undergo invasive surgery to treat the seizures and this is common in seizures that are caused by tumors or physical brain damage. Non-invasive surface electrode EEG measurement gives an estimate of the seizure onset but more invasive intracranial electrocorticogram (ECoG) are required at times for precise localization of the epileptogenic zone. This project aims at designing and implementing a device that can be used to detect and monitor the attention and meditation values of a person in real time. The system measures the EEG waves of the brain, performs feature extraction, classification and sends the control command over wireless to a remote controller. The remote controller in turn issues commands with corresponding brain wave frequency and sends it to the cloud for remote analysis and classification.
References
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TL;DR: A novel scheme of emotion-specific multilevel dichotomous classification (EMDC) is developed and compared with direct multiclass classification using the pLDA, with improved recognition accuracy of 95 percent and 70 percent for subject-dependent and subject-independent classification, respectively.
Abstract: Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological data set to a feature-based multiclass classification. In order to collect a physiological data set from multiple subjects over many weeks, we used a musical induction method that spontaneously leads subjects to real emotional states, without any deliberate laboratory setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity, and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, and positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. An improved recognition accuracy of 95 percent and 70 percent for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.
953 citations
TL;DR: These data appear to be the first to distinguish valence and intensity of musical emotions on frontal electrocortical measures.
Abstract: Using recent regional brain activation/emotion models as a theoretical framework, we examined whether the pattern of regional EEG activity distinguished emotions induced by musical excerpts which were known to vary in affective valence (i.e., positive vs. negative) and intensity (i.e., intense vs. calm) in a group of undergraduates. We found that the pattern of asymmetrical frontal EEG activity distinguished valence of the musical excerpts. Subjects exhibited greater relative left frontal EEG activity to joy and happy musical excerpts and greater relative right frontal EEG activity to fear and sad musical excerpts. We also found that, although the pattern of frontal EEG asymmetry did not distinguish the intensity of the emotions, the pattern of overall frontal EEG activity did, with the amount of frontal activity decreasing from fear to joy to happy to sad excerpts. These data appear to be the first to distinguish valence and intensity of musical emotions on frontal electrocortical measures.
473 citations
TL;DR: The study of emotional effects of music is handicapped by a lack of appropriate research paradigms and methods, due to a dearth of conceptual-theoretical analyses of the process underlying emotion production via music as mentioned in this paper.
Abstract: The study of emotional effects of music is handicapped by a lack of appropriate research paradigms and methods, due to a dearth of conceptual-theoretical analyses of the process underlying emotion production via music. It is shown that none of the three major assessment methods for emotion induction – lists of basic emotions, valence-arousal dimensions, and eclectic emotion inventories – is well suited to the task. By focusing on a small number of evolutionarily continuous basic emotions one downplays the more complex forms of emotional processes in humans, especially affective feeling states produced by music which do not serve adaptive behavioral functions. Similarly, a description of emotional effects of music limited to valence and arousal gradations precludes assessment of the kind of qualitative differentiation required by the study of the subtle emotional effects of music. Finally, eclectic lists of emotions generated by researchers to suit the needs of a particular study may lack validity and reli...
434 citations
TL;DR: The old concept stating that EEG alpha (10-Hz) activity reflects passive or idling states of the brain is giving way to modern views of 10-Hz oscillations in relation to diverse brain functions comprising sensory, motor, and memory processes.
369 citations
01 Mar 1996
TL;DR: The experimental condition consisted of 40 45-minute sessions of training in enhancing beta activity and suppressing theta activity, spaced over 6 months as mentioned in this paper, and the experimental group demonstrated a significant increase (mean of 9 points) on the K-Bit IQ Composite as compared to the control group (p<.05).
Abstract: Eighteen children with ADD/ADHD, some of whom were also LD, ranging in ages from 5 through 15 were randomly assigned to one of two conditions. The experimental condition consisted of 40 45-minute sessions of training in enhancing beta activity and suppressing theta activity, spaced over 6 months. The control condition, waiting list group, received no EEG biofeedback. No other psychological treatment or medication was administered to any subjects. All subjects were measured at pretreatment and at posttreatment on an IQ test and parent behavior rating scales for inattention, hyperactivity, and aggressive/defiant (oppositional) behaviors. At posttreatment the experimental group demonstrated a significant increase (mean of 9 points) on the K-Bit IQ Composite as compared to the control group (p<.05). The experimental group also significantly reduced inattentive behaviors as rated by parents (p<.05). The significant improvements in intellectual functioning and attentive behaviors might be explained as a result of the attentional enhancement affected by EEG biofeedback training. Further research utilizing improved data collection and analysis, more stringent control groups, and larger sample sizes are needed to support and replicate these findings.
315 citations