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In recent days, the classification of abnormal brain Electroencephalogram (EEG) signals is a demanding and challenging task. For this purpose, some of the classification techniques which include Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) are frequently used in the existing works. But, it has some drawbacks such as, the above mentioned techniques...
In this study, we have generated four active commands using hybrid electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for quadcopter control in online environment. Mental arithmetic, left hand clench imagery, and left and right eye-movements are used to navigate the quadcopter. Mental arithmetic task (decoded by fNIRS from the prefrontal cortex) is used to move the quadcopter...
This paper addresses the potential of the Brain Computer Interface (BCI) for self-quantification through recording and analysis of brain activity. From the electroencephalographic (EEG) signal it is possible to quantify and investigate brain activity, allowing, for example, a measure of engagement with tasks to be derived, states of relaxation or anxiety to be determined, or levels of alertness to...
In this paper we propose and discuss a new classification method of motor imagery tasks based on patterns and Euclidean distance. The proposed method is simple, fast, but considerably sensitive with respect to the selected features/frequencies for classification. Choosing a predefined number of features leads to results similar to GTEC/BCI2000 while an optimal selection gives improved results but...
In BCI applications for stroke rehabilitation, BCI systems are used with the aim of providing patients with an instrument that is capable of monitoring and reinforcing EEG patterns generated by motor imagery (MI). In this study we proposed an offline analysis on data acquired from stroke patients subjected to a BCI-assisted MI training in order to define an index for the evaluation of MI-BCI training...
In this paper, we investigate the feasibility of employing robotics, high-density electroencephalography (EEG), and surface electromyography (EMG) for ankle rehabilitation in a subject with multiple sclerosis (MS). A single session of seated, interactive ankle robot (“Anklebot”) training with concurrent 60-channel EEG and 2-channel EMG monitoring was conducted. The task entailed pointing with the...
CBER (content-based-EEG-retrieval) systems present short data segments as query samples for similar segments in EEG databases. These systems have many applications in large-scale data-mining, but require effective and verifiable retrieval strategies. This paper introduces a new feature strategy based on class probabilities calculated by LIBSVM classification using low-order autoregressive (AR) modeling...
Every year millions of people worldwide suffer from stroke, resulting in motor and/or cognitive disability. As a result, patients experience an increased loss of independence, autonomy and low self-esteem. Evolving to a chronic condition, stroke requires of continuous rehabilitation and therapy. Current ICT approaches, with the use of robotics and Virtual Reality, show some benefits over conventional...
Amplitude-integrated electroencephalographic (aEEG), a method for continuous long-term monitoring of brain activities, is widely used for clinical needs in monitoring newborns. While the variation in aEEG signals from different individuals causes differences in the data distribution, the task to model and automatically classify aEEG signals across different individuals is challenging. In this paper,...
The processing of a patient in a medical facility encompasses data acquisition, data analysis, diagnosis and medical reporting. The result is a program of treatment for the patient. It is postulated that the latter two stages can be facilitated by allowing acquisition locations to avail of data interpretation by multiple off-site analysts who interactively annotate patient data in a collaborative...
Clinical electroencephalography (EEG) is routinely used to monitor brain function in critically ill patients, and specific EEG waveforms are recognized by clinicians as signatures of abnormal brain. These pathologic EEG waveforms, once detected, often necessitate accute clinincal interventions, but these events are typically rare, highly variable between patients, and often hard to separate from background,...
Medical Informatics is the scientific field that deals with the storage, retrieval and optimal use of information and data in medicine. It is often called healthcare informatics or biomedicai informatics, and forms part of the wider domain of eHealth. The end objective of biomedicai informatics is the coalescing of data, knowledge, and the tools necessary to apply that data and knowledge in the decision-making...
Driver's cognitive state monitoring has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. In our past studies, we found that the EEG power spectrum changes were highly correlated with the driver's driving behavior performance. In this study, we attempt to construct an EEG-based self-constructing neural fuzzy system to...
The learning of a novel task currently rely heavily on conventional classroom instruction with qualitative assessment and observation. Introduction of individualized tutorials with integrated neuroscience-based evaluation techniques could significantly accelerate skill acquisition and provide quantitative evidence of successful training. We have created a suite of adaptive and interactive neuro-educational...
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