Bacteria Hunt: A multimodal, multiparadigm BCI game

Brain-Computer Interfaces (BCIs) allow users to control applications by brain activity. Among their possible applications for non-disabled people, games are promising candidates. BCIs can enrich game play by the mental and affective state information they contain. During the eNTERFACE’09 workshop we developed the Bacteria Hunt game which can be played by keyboard and BCI, using SSVEP and relative alpha power. We conducted experiments in order to investigate what difference positive vs. negative neurofeedback would have on subjects’ relaxation states and how well the different BCI paradigms can be used together. We observed no significant difference in mean alpha band power, thus relaxation, and in user experience between the games applying positive and negative feedback. We also found that alpha power before SSVEP stimulation was significantly higher than alpha power during SSVEP stimulation indicating that there is some interference between the two BCI paradigms

P300 detection and characterization for brain computer interface

Advances in cognitive neuroscience and brain imaging technologies have enabled the brain to directly interface with the computer. This technique is called as Brain Computer Interface (BCI). This ability is made possible through use of sensors that can monitor some of the physical processes that occur inside the brain. Researchers have used these kinds of technologies to build brain-computer interfaces (BCIs). Computers or communication devices can be controlled by using the signals produced in the brain. This can be a real boon for all those who are not able to communicate with the outside world directly. They can easily forecast their emotions or feelings using this technology. In BCI we use oddball paradigms to generate event-related potentials (ERPs), like the P300 wave, on targets which have been selected by the user. The basic principle of a P300 speller is detection of P300 waves that allows the user to write characters. Two classification problems are encountered in the P300 speller. The first is to detect the presence of a P300 in the electroencephalogram (EEG). The second one refers to the combination of different P300 signals for determining the right character to spell. In this thesis both parts i.e., the classification as well as characterization part are presented in a simple and lucid way. First data is obtained using data set 2 of the third BCI competition. The raw data was processed through matlab software and the corresponding feature matrices were obtained. Several techniques such as normalization, feature extraction and feature reduction of the data are explained through the contents of this thesis. Then ANN algorithm is used to classify the data into P300 and no-P300 waves. Finally character recognition is carried out through the use of multiclass classifiers that enable the user to determine the right character to spell

P300 Detection for Brain Computer Interface

P300 based brain computer interface (BCI) sometimes called brain machine interface (BMI) is a way of direct communication between human brain and external device which provides an alternative communication link with outside world to the people who are unable to communicate via conventional means because of sever motor disability. P300 wave is an event related potential which evoked in the process of decision making of human brain which can be generated using oddball paradigm. This thesis aims to detect the P300 wave as accurate as possible. To do that this study proposed discrete wavelet transforms (DWT) based feature extraction method from each P300 and No-P300 of EEG signal from the entire 64 channel. Principal component analysis (PCA) technique is further applied for the reduction of the dimension of the feature. Detection of P300 is achieved using support vector machine (SVM) and artificial neural network (ANN) classifier. Experimental result shows that the proposed method with SVM classifier yields better performance compared to the method with ANN

Evaluate Cognitive Evoked Potential and Serum Adiponectin Level in Prediabetes: A Cross Sectional study

BACKGROUND: Prediabetes is more common in young adults. Prediabetes (Impaired Fasting Glucose and Impaired Glucose Tolerance) leads to increased risk of cerebrovascular, cardiovascular changes and overt diabetes. The worldwide prevalence of Impaired Glucose tolerance(IGT) was found to be 343 million (7.8%). Pre-diabetics have an increased risk of developing cerebrovascular and cardiovascular changes. Many studies have shown that there is increased risk of developing cognitive dysfunction in diabetic individuals. Some studies reveal that pre-diabetics are prone for cognitive dysfunction. These changes are likely due to hyperglycemia, insulin resistance, vascular changes and hypoglycaemia. So the objective of this study is to assess the early onset cognitive dysfunction in Pre-diabetics. Adiponectin is an adipocyte derived hormone that plays a protective role against insulin resistance and inflammation and also protects the body against metabolic diseases. It has anti-inflammatory and anti-atherogenic effects. In brain, adiponectin improves insulin signaling and glucose uptake Adiponectin level is altered both in insulin resistance and cognitive impairment. So adiponectin can be taken as a marker to evaluate the cognition in pre-diabetic individuals. Long latency evoked potentials are related to cognitive processing and are referred to as cognitive evoked potential (CEP). P300 is the most frequently investigated CEP appearing at about 300 ms following task-related stimuli. This test reflects the subject's cognitive skill level and verifies whether disorders are present in the auditory association cortex. So cognitive evoked potential is used to evaluate the early cognitive dysfunctions in Pre-diabetic individuals. The earliest changes in cognitive dysfunctions can be picked by evaluating patient’s cognitive evoked potential. And also adiponectin has a significant role in cognitive functions. So in this study, the objective is to evaluate the cognition in pre-diabetic individuals by assessing the patient’s cognitive evoked potential and serum adiponectin level. AIM OF THE STUDY: To evaluate whether cognition is impaired in the pre-diabetic patients when compared to that of clinically healthy individuals and to find its association with serum adiponectin level in the pre-diabetic patients OBJECTIVES: 1. To estimate the change in variables of cognitive evoked potential response in the Pre-diabetic individuals and to compare the same with clinically normal healthy individuals of the study group. 2. To estimate and compare the variations in serum adiponectin level between the prediabetic patients and clinically normal healthy individuals. 3. To find if any association exists between the serum adiponectin level and parameters of the cognitive evoked potential response like latency (ms) and amplitude (mvolt) among the study group. MATERIALS & METHODS: This is a descriptive cross-sectional study conducted during the year 2018-19 in Rajiv Gandhi Government General Hospital (RGGGH), Madras Medical College. This study consisted of fifty prediabetic patients in the age group of 25 – 50 years who were selected based on ADA prediabetic guidelines (case study group) and fifty clinically healthy individuals of both the gender in the age group of 25-50 years (control group). The BMI, the fasting blood glucose levels, the post load blood glucose levels were obtained. The parameters of cognitive evoked potential (N1 latency, P2 latency, N2 latency, P300 latency and N2-P300 amplitude) were recorded. Serum adiponectin levels were measured using ELISA. RESULTS: Prediabetic patients showed cognitive decline with prolonged latencies (N1, N2, P2 and P300) and decrease in N2-P300 amplitude which were statistically significant (P<0.05). Serum adiponectin was increased significantly in prediabetic patients than clinically healthy normal individuals (P<0.05). Serum adiponectin correlated positively with P300 latency and serum adiponectin correlated negatively with N2-P300 amplitude which was statistically significant (P<0.05). CONCLUSION: Prediabetic patients showed a significant prolongation in latencies and decrease in N2-P300 amplitude when compared to that of clinically healthy normal individuals. Also serum adiponectin levels were increased in prediabetic patients significantly and its levels correlated negatively N2-P300 amplitude. So we can conclude from the present study that the prediabetic patients should be screened for cognitive decline to detect early cognitive impairment if any

Evaluating a Novel Brain-Computer Interface and EEG Biomarkers For Cognitive Assessment in Children With Cerebral Palsy

Standardized neuropsychological assessments and research instruments are typically administered with verbal queries, pictures and manipulatives that require verbal or motor responses. Thus, they are often inaccessible to people with physical and communicative impairments. The goal of this dissertation was to investigate alternative approaches that do not require any motor or speech input to assess cognitive capacity of an individual. The first approach involved using a brain-computer interface (BCI) that was adapted to facilitate the administration of a Peabody Picture Vocabulary Test (PPVT-IV). Which is a receptive vocabulary assessment than can be used as a proxy for intelligence. The second approach was to use brain dynamics such as functional connectivity and bandpass analysis to assess cognitive capacity of an individual. We then tested these two approaches on typically developing (TD) individuals (N=11) people with cerebral palsy (CP) (N=18). Our results suggest that children with cerebral palsy show signs of lower intelligence than typically developing children when using functional connectivity and power band analysis, however, they performed equally well in the PPVT-IV. We believe this is due to the neural compensation resulting from the subjects’ pathology. Thus, the preferred method for assessing cognitive measures in an individual with severe motoric impairments is a BCI. By using a BCI, a user can respond to standardized cognitive assessments that already have well established norms. However, it is important to make sure that when designing these systems, the changes made to adapt the cognitive assessment for the BCI do not alter the format and psychometrics of the test. Our BCI able to maintain the psychometrics of a PPVT-IV test and perform with an accuracy of 97.78 ± 4.06. In addition, scores on the BCI-facilitated PPVT-IV and the standard PPVT-IV were highly correlated (r = 0.95, p<0.001) with a mean difference of 2.0 ± 6.4 points, which is within the standard error of the PPVT-IV. Thus, our BCI-facilitated PPVT-IV provided comparable results to the standard PPVT-IV, suggesting that populations for whom standardized cognitive tests are not accessible could benefit from our BCI-facilitated approach.PHDNeuroscienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136949/1/pharoram_1.pd

Adaptive techniques for the detection and localization of event related potentials from EEGs using reference signals

In this thesis we show the methods we developed for the detection and localisation of P300 signals from the electroencephalogram. We utilised signal processing theory in order to enhance the current methodology. The work done can be applied both to EEG averages and single trial EEG data. We developed a variety of methods dealing with the extraction of the P300 and its subcomponents using independent component analysis and least squares. Moreover, we developed novel localisation methods that localise the desired P300 subcomponent from EEG data. Throughout the thesis the main idea was the use of reference signals, which describe the prior information we have about the sources of interest. The main objective of this thesis is to utilize adaptive techniques, namely blind source separation (BSS), least squares (LS) and spatial filtering, in order to extract the P300 subcomponents from the electroencephalogram (EEG) with greater accuracy than the traditional methods. The first topic of research, is the development of constrained BSS and blind signal extraction (BSE) algorithms, to enhance the estimation of the conventional BSS and BSE algorithms. In these methods we use reference signals as prior information, obtained from real EEG data, to aid BSS and BSE in the extraction of the P300 subcomponents. Although, this method exhibits very good behaviour in terms of EEG averaged data, its performance degrades when applied to single trial data, which is the response of the brain after one single stimulus. The second topic deals with single trial EEG data and is based on least squares. Again, we use reference signals to describe the prior knowledge of the P300 subcomponents. In contrast to the first method, the reference signals are Gaussian spike templates with variable latency and width. The target of this algorithm is to measure the properties of the extracted P300 subcomponents and obtain features that can be used in the classification of schizophrenic patients and healthy subjects. Finally, the idea of spatial filtering combined with the use of a reference signal for localisation is introduced for the first time. The designed algorithm localises our desired source from within a mixture of sources where the propagation model of the sources is available. It performs well in the presence of noise and correlated sources. The research presented in this thesis paves the path in introducing adaptive techniques based on reference signals into ERP estimation. The results have been very promising and provide a big step in establishing a foundation for future research

De animais a máquinas : humanos tecnicamente melhores nos imaginários de futuro da convergência tecnológica

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Sociais, Departamento de Sociologia, 2020.O tema desta investigação é discutir os imaginários sociais de ciência e tecnologia que emergem a partir da área da neuroengenharia, em sua relação com a Convergência Tecnológica de quatro disciplinas: Nanotecnologia, Biotecnologia, tecnologias da Informação e tecnologias Cognitivas - neurociências- (CT-NBIC). Estas áreas desenvolvem-se e são articuladas por meio de discursos que ressaltam o aprimoramento das capacidades físicas e cognitivas dos seres humanos, com o intuito de construir uma sociedade melhor por meio do progresso científico e tecnológico, nos limites das agendas de pesquisa e desenvolvimento (P&D). Objetivos: Os objetivos nesse cenário, são discutir as implicações éticas, econômicas, políticas e sociais deste modelo de sistema sociotécnico. Nos referimos, tanto as aplicações tecnológicas, quanto as consequências das mesmas na formação dos imaginários sociais, que tipo de relações se estabelecem e como são criadas dentro desse contexto. Conclusão: Concluímos na busca por refletir criticamente sobre as propostas de aprimoramento humano mediado pela tecnologia, que surgem enquanto parte da agenda da Convergência Tecnológica NBIC. No entanto, as propostas de melhoramento humano vão muito além de uma agenda de investigação. Há todo um quadro de referências filosóficas e políticas que defendem o aprimoramento da espécie, vertentes estas que se aliam a movimentos trans-humanistas e pós- humanistas, posições que são ao mesmo tempo éticas, políticas e econômicas. A partir de nossa análise, entendemos que ciência, tecnologia e política estão articuladas, em coprodução, em relação às expectativas de futuros que são esperados ou desejados. Ainda assim, acreditamos que há um espaço de diálogo possível, a partir do qual buscamos abrir propostas para o debate público sobre questões de ciência e tecnologia relacionadas ao aprimoramento da espécie humana.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The subject of this research is to discuss the social imaginaries of science and technology that emerge from the area of neuroengineering in relation with the Technological Convergence of four disciplines: Nanotechnology, Biotechnology, Information technologies and Cognitive technologies -neurosciences- (CT-NBIC). These areas are developed and articulated through discourses that emphasize the enhancement of human physical and cognitive capacities, the intuition it is to build a better society, through the scientific and technological progress, at the limits of the research and development (R&D) agendas. Objectives: The objective in this scenery, is to discuss the ethic, economic, politic and social implications of this model of sociotechnical system. We refer about the technological applications and the consequences of them in the formation of social imaginaries as well as the kind of social relations that are created and established in this context. Conclusion: We conclude looking for critical reflections about the proposals of human enhancement mediated by the technology. That appear as a part of the NBIC technologies agenda. Even so, the proposals of human enhancement go beyond boundaries that an investigation agenda. There is a frame of philosophical and political references that defend the enhancement of the human beings. These currents that ally to the transhumanism and posthumanism movements, positions that are ethic, politic and economic at the same time. From our analysis, we understand that science, technology and politics are articulated, are in co-production, regarding the expected and desired futures. Even so, we believe that there is a space of possible dialog, from which we look to open proposals for the public discussion on questions of science and technology related to enhancement of human beings