Fetal Electrocardiogram Signal Extraction by ANFIS Trained with PSO Method

Studies indicate that the primary source of distress in pregnent mothers is their concerns about fetus’s condition and health. One way to know about condition of fetus is non-invasive fetal electrocardiogram signal extraction through which the components of fetal electrocardiogram signal are extracted from a signal recorded at abdominal area of mother which is a combination of fetal and maternal electrocardiogram signal and noise source components. The purpose of this study is to propose an algorithm to boost this extraction. To this end, we decomposed electrocardiogram signal to its Intrinsic Mode Functions (IMFs) thruogh Empirical Mode Decomposition algorithm; then, we removed the last and collected the other IMFs to reconstruct electrocardiogram signal without Baseline. Afterwards, we used Particle Swarm Optimization to train and adjust the parameters of Adaptive Neuro-Fuzzy Inference System to model the path that maternal electrocardiogram signal travel to reach abdominal area. Accordingly, we were able to distinguish and remove maternal electrocardiogram signal components from the recorded signal and hence we obtained a good approximation of fetal electrocardiogram signal. We implemented our algorithm and other algorithms on simulated and real signals and found out that, in most cases, the proposed algorithm improved the extraction of fetal electrocardiogram signal.DOI:http://dx.doi.org/10.11591/ijece.v2i2.23

Enhancement of Sleep Quality and Stability Using Acoustic Stimulation During Slow Wave Sleep

Background: One of the challenges today is that the quality of sleep has weakened by many external factors, which we are not even aware of and which directly affect sleep. Sleep quality has an essential role in maintaining the cognitive function and memory consolidation of individuals. So far, various studies have been done to improve the quality of sleep by using external electrical stimulation, vestibular and olfactory system stimulation.Methods: In this study, the increase in sleep quality was considered by simultaneous acoustic stimulation in a deep sleep to increase the density of slow oscillations. Slow oscillations are the important events recorded in electroencephalography (EEG) and hallmark deep sleep. Acoustic stimulation of pink noise with random frequency ranging from 0.8 to 1.1 Hz was used to improve sleep quality.Results: Eight healthy adults (mean age: 28.4±7.8 years) studied in 3 nights under 3 conditions: accommodation night, stimulation night (STIM) and no stimulation night (SHAM), in counter-balanced order. Significant characteristics of the objective and subjective quality of sleep have been extracted from the acquired EEG and compared in the last 2 nights. Also, the arousal and cyclic alternating pattern characteristics have been measured to assess sleep stability over 2 nights of STIM and SHAM.Conclusion: Our findings confirm this goal of the study that applying designed acoustic stimulation simultaneously in the slow wave sleep (SWS) stage increases the duration of deep sleep and ultimately improves overall sleep stability and quality

Classification of Mental Stress Levels by Analyzing fNIRS Signal Using Linear and Non-linear Features

Background: Mental stress is known as one of the main influential factors in development of different diseases including heart attack and stroke. Thus, quantification of stress level can be very important in preventing many diseases and in human health.Methods: The prefrontal cortex is involved in body regulation in response to stress. In this research, functional near infrared spectroscopy (fNIRS) signals were recorded from FP2 position in the international electroencephalographic 10–20 system during a stressful mental arithmetic task to be calculated within a limited period of time. After extracting the brain’s hemodynamic response from fNIRS signal, different linear and nonlinear features were extracted from the signal which are then used for stress levels classification both individually and in combination.Results: In this study, the maximum accuracy of 88.72% was achieved in classification between high and low stress levels, and 96.92% was obtained for the stress and rest states.Conclusion: Our results showed that using the proposed linear and nonlinear features it is possible to effectively classify stress levels from fNIRS signals recorded from only one site in the prefrontal cortex. Comparing to other methods, it is shown that the proposed algorithm outperforms other previously reported methods using the nonlinear features extracted from the fNIRS signal. These results clearly show the potential of fNIRS signal as a useful tool for early diagnosis and quantify stress

Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework

A conventional subject-dependent (SD) brain-computer interface (BCI) requires a complete data-gathering, training, and calibration phase for each user before it can be used. In recent years, a number of subject-independent (SI) BCIs have been developed. However, there are many problems preventing them from being used in real-world BCI applications. A weaker performance compared to the subject-dependent (SD) approach, and a relatively large model requiring high computational power are the most important ones. Therefore, a potential real-world BCI would greatly benefit from a compact low-power subject-independent BCI framework, ready to be used immediately after the user puts it on. To move towards this goal, we propose a novel subject-independent BCI framework named CCSPNet (Convolutional Common Spatial Pattern Network) trained on the motor imagery (MI) paradigm of a large-scale electroencephalography (EEG) signals database consisting of 21600 trials for 54 subjects performing two-class hand-movement MI tasks. The proposed framework applies a wavelet kernel convolutional neural network (WKCNN) and a temporal convolutional neural network (TCNN) in order to represent and extract the diverse spectral features of EEG signals. The outputs of the convolutional layers go through a common spatial pattern (CSP) algorithm for spatial feature extraction. The number of CSP features is reduced by a dense neural network, and the final class label is determined by a linear discriminative analysis (LDA) classifier. The CCSPNet framework evaluation results show that it is possible to have a low-power compact BCI that achieves both SD and SI performance comparable to complex and computationally expensive.Comment: 15 pages, 6 figures, 6 tables, 1 algorith

First Event-Related Potentials Evidence of Auditory Morphosyntactic Processing in a Subject-Object-Verb Nominative-Accusative Language (Farsi)

While most studies on neural signals of online language processing have focused on a few—usually western—subject-verb-object (SVO) languages, corresponding knowledge on subject-object-verb (SOV) languages is scarce. Here we studied Farsi, a language with canonical SOV word order. Because we were interested in the consequences of second-language acquisition, we compared monolingual native Farsi speakers and equally proficient bilinguals who had learned Farsi only after entering primary school. We analyzed event-related potentials (ERPs) to correct and morphosyntactically incorrect sentence-final syllables in a sentence correctness judgment task. Incorrect syllables elicited a late posterior positivity at 500–700 ms after the final syllable, resembling the P600 component, as previously observed for syntactic violations at sentence-middle positions in SVO languages. There was no sign of a left anterior negativity (LAN) preceding the P600. Additionally, we provide evidence for a real-time discrimination of phonological categories associated with morphosyntactic manipulations (between 35 and 135 ms), manifesting the instantaneous neural response to unexpected perturbations. The L2 Farsi speakers were indistinguishable from L1 speakers in terms of performance and neural signals of syntactic violations, indicating that exposure to a second language at school entry may results in native-like performance and neural correlates. In nonnative (but not native) speakers verbal working memory capacity correlated with the late posterior positivity and performance accuracy. Hence, this first ERP study of morphosyntactic violations in a spoken SOV nominative-accusative language demonstrates ERP effects in response to morphosyntactic violations and the involvement of executive functions in non-native speakers in computations of subject-verb agreement.Peer Reviewe

Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods

Background: Emotion recognition, as a subset of affective computing, has received considerable attention in recent years. Emotions are key to human-computer interactions. Electroencephalogram (EEG) is considered a valuable physiological source of information for classifying emotions. However, it has complex and chaotic behavior.Methods: In this study, an attempt is made to extract important nonlinear features from EEGs with the aim of emotion recognition. We also take advantage of machine learning methods such as evolutionary feature selection methods and committee machines to enhance the classification performance. Classification performed concerning both arousal and valence factors.Results: Results suggest that the proposed method is successful and comparable to the previous works. A recognition rate equal to 90% achieved, and the most significant features reported. We apply the final classification scheme to 2 different databases including our recorded EEGs and a benchmark dataset to evaluate the suggested approach.Conclusion: Our findings approve of the effectiveness of using nonlinear features and a combination of classifiers. Results are also discussed from different points of view to understand brain dynamics better while emotion changes. This study reveals useful insights about emotion classification and brain-behavior related to emotion elicitation

A Review on EEG Signals Based Emotion Recognition

Emotion recognition has become a very controversial issue in brain-computer interfaces (BCIs). Moreover, numerous studies have been conducted in order to recognize emotions. Also, there are several important definitions and theories about human emotions. In this paper we try to cover important topics related to the field of emotion recognition. We review several studies which are based on analyzing electroencephalogram (EEG) signals as a biological marker in emotion changes. Considering low cost, good time and spatial resolution, EEG has become very common and is widely used in most BCI applications and studies. First, we state some theories and basic definitions related to emotions. Then some important steps of an emotion recognition system like different kinds of biologic measurements (EEG, electrocardiogram [EEG], respiration rate, etc), offline vs online recognition methods, emotion stimulation types and common emotion models are described. Finally, the recent and most important studies are reviewed

Evaluating the Determinism of Brain Signals Using Recurrence Chaotic Features in Positive, Negative and Neutral Emotional States in the Sources Achieved From ICA Algorithm

Background: This study investigates electroencephalogram (EEG) signals in positive, negative and neutral emotion states.Method: It is assumed that the brain draws on several independent sources in any activity that are observable by independent component algorithm (ICA). To overcome the problem of ill-posedness of extracted components from ICA algorithm, first these sources are sorted out by Shannon entropy and then based on these sources, the features of trapping time and determinism of Recurrence Quantification Analysis (RQA) are extracted as representative of determination.Result: The results show that the degree of determinism of sorted sources related by emotions is significantly different over time and in three positive, negative and neutral states. The degree of determinism increases in neutral, positive and negative emotional states respectively

Rehabilitation to Handicapped for Communication with Computer via Type of Letter by Eye Movement

Objective: Nowadays computers are controlled by hand with some tools like mouse and keyboard generally, so people that can't use their hands aren't able to use these tools. This research was aimed to design a system, which help handicapped people to type by their eye movement. Materials & Methods: In this design, boarder between iris and sclera which is called limbus is tracked with the Infra Red transmitting and receiving system. Then software will process measured values, and will change them to proper values for moving cursor on characters. Since changing the user, unwanted head movements can affect on system operation, a calibration stage is considered which adjust the system in new condition. The system was implemented on 10 subjects (5 women & 5 men) and data were analyzed.  Results: Distance resolution of system on 15" monitor's screen is about 4.11±3.38 centimeters. People with no experience can type a sentence with 14 characters in 3:30 minute by 28% error and an experienced person can type this sentence in 3 minute with 13% error. So the system with relatively high repeatability allows the user to communicate with computer by his eye movements. Conclusion: This system is able to recognize 12 points on monitor's screen. Each point is belonging to one character. Facility of using the system and no contact between system and eye are from the benefits of the system