Asynchronous Brain Machine Interface System for Autonomous Assembly Robot Based on Facial Expression Paradigm

Brain-computer interfaces (BCI) provide direct and non-muscular communication methods for the people with severe motor impairments. Event-related potentials (ERPs) as efficient modals are commonly used in some of the BCI systems, including visual stimulus, auditory stimulus as well as tactile stimulus. In this experiment, the corresponding Chinese pronunciations were inserted into the visual Oddball series of 1–9 numbers to carry out the cross-sense stimuli of BCI. The experimental data analysis result proves that the P300 components produced by visual-auditory associate stimulation have higher amplitudes and shorter latencies than those produced by visual-only stimulus. For further analysis the constrained independent component analysis (cICA) method was applied when extracting the signal features of ERP and the support vector machine (SVM) method was used to BCI classification. Result proves that the ERPs produced by visual-auditory associate stimulation modal have better recognition efficiency than those in visual-only stimulation. It can relevant the capacity of information alteration in BCI and is worth to do more studies.

Objective. Brain–machine interfaces (BMI) are an emerging therapeutic option that can allow paralyzed patients to gain control over assistive technology devices (ATDs). BMI approaches can be broadly classified into invasive (based on intracranially implanted electrodes) and noninvasive (based on skin electrodes or extracorporeal sensors). Invasive BMIs have a favorable signal-to-noise ratio, and thus allow for the extraction of more information than noninvasive BMIs, but they are also associated with the risks related to neurosurgical device implantation. Current noninvasive BMI approaches are typically concerned, among other issues, with long setup times and/or intensive training. Recent studies have investigated the attitudes of paralyzed patients eligible for BMIs, particularly patients affected by amyotrophic lateral sclerosis (ALS). These studies indicate that paralyzed patients are indeed interested in BMIs. Little is known, however, about the degree of knowledge among paralyzed patients concerning BMI approaches or about how patients retrieve information on ATDs. Furthermore, it is not yet clear if paralyzed patients would accept intracranial implantation of BMI electrodes with the premise of decoding improvements, and what the attitudes of a broader range of patients with diseases such as stroke or spinal cord injury are towards this new kind of treatment. Approach. Using a questionnaire, we surveyed 131 paralyzed patients for their opinions on invasive BMIs and their attitude toward invasive BMI treatment options. Main results. The majority of the patients knew about and had a positive attitude toward invasive BMI approaches. The group of ALS patients was especially open to the concept of BMIs. The acceptance of invasive BMI technology depended on the improvements expected from the technology. Furthermore, the survey revealed that for paralyzed patients, the Internet is an important source of information on ATDs. Significance. Websites tailored to prospective BMI users should be further developed to provide reliable information to patients, and also to help to link prospective BMI users with researchers involved in the development of BMI technology.

Identification of gait imagery based on fNIRS and class-dependent sparse representation

Many research on how the human brain works has been done in the last century. The use of electroencephalogram signal generated from quantifying the brain wave have been developed in many areas including the development of brain computer interface (BCI) concept. One type of BCI that interesting for the future use is motor imagery (MI) based-BCI which only requiring imagination of a person to control an object. This study proposed a feature extraction in eight different channels using discrete wavelet (DWT) coefficients. The wavelet coefficient is transformed to frequency domain using discrete fourier transform (DFT) and then average power spectrum is calculated. Level 5 of detail component of the DWT is chosen because from 512Hz sampling frequency (8 - 16Hz), it resemble mu rhythm of brain wave (8 - 12Hz) which affected from motor imagery activity. The classification of three classes, which are imagination of right body movement, left movement, and random word using multiclass support vector machine (SVM) shows a promising result with sensitivity of 96.88%, 86.12% and 52.78% from three different subjects.

Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.

One of the main problems in Brain Computer Interface (BCI) systems is the non-stationary behavior of the electroencephalography (EEG) signals causing problems in real time applications. Another common problem in BCI systems is the situation where the labeled data are scarce. In this study, we take a semi-supervised learning perspective and propose solving both types of problems by updating the BCI system with labels obtained from the outputs of the classifier. To test the approach, data from motor imagery BCI system are used. Attributes extracted from EEG signals are classified with Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). With respect to the static classifiers, accuracy was improved approximately 4% using the proposed adaptation approach in the case of a training dataset. Even though the difference between the performance of static and adaptive classifiers decreases as the size of training data increases, the accuracy of our proposed adaptive classifier remains higher. The proposed approach has also improved the performance of a BCI system around 4% in the case of non-stationary signals as well.

Wajah adalah bagian tubuh manusia yang berfungsi sebagai pusat ekspresi, pengenalan dan juga komunikasi. Penciptaan teknik yang berguna untuk mengidentifikasi dan menganalisis ekspresi wajah sangat penting untuk penelitian ini. Support Vector Machine (SVM) merupakan teknik pembelajaran mesin yang digunakan untuk pengenalan pola klasifikasi ekspresi wajah Manusia. Support Vector Machine (SVM) adalah metode klasifikasi yang menggunakan konsep mencari hyperplane yang optimal dalam suatu ruang feature untuk memisahkan beberapa kelas. Dari keempat klasifikasi tersebut ternyata presentasi accuracy, precision dan recall tidak berbeda jauh untuk mendeteksi ekspresi wajah dari tiap kelas tersebut dengan menggunakan metode SVM. Berdasarkan uji ekstraksi hasil rata-rata accuracy tertinggi yaitu jenis ekspresi marah dengan accuracy paling tinggi. Tingkat rata-rata tertinggi accuracy yang kedua yaitu ekspresi senang. tingkat rata-rata tertinggi accuracy ketiga yaitu ekspresi netral, lalu tingkat rata-rata tertinggi accuracy keempat yaitu ekspresi senyum dan tingkat rata-rata terendah accuracy yaitu ekspresi sedih. The face is a part of the human body that functions as a center of expression, recognition and communication. The creation of useful techniques for identifying and analyzing facial expressions was crucial for this study. Support Vector Machine (SVM) is a machine learning technique used for pattern recognition classification of Human facial expressions. Support Vector Machine (SVM) is a classification method that uses the concept of finding the optimal hyperplane in a feature space to separate classes. From the four classifications, it turns out that the presentation of accuracy, precision and recall is not much different to detect facial expressions from each class using the SVM method. Based on the extraction test, the highest average accuracy result is the type of angry expression with the highest accuracy. The second highest average level of accuracy is the expression of pleasure. The third highest average level of accuracy is neutral expression, then the fourth highest average level of accuracy is smile expression and the lowest average level of accuracy is sad expression.

At present, it is an important issue to obtain accurate classification of EEG signal in brain-computer interface. We collected the EEG data which are recorded at a sampling rate of 250Hz by EGI-64 scalp electrodes placed according to the international 10/20 system. Firstly, discrete wavelet transform method is used to decompose the average power of the channel C3/C4 and P3/P4 in left and right hands imagine movement during the some experimental period of time. The reconstructed signal of approximation coefficient A6 on the sixth level is selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis and Support Vector Machine methods are compared. The final classifications results show that correct classification rate by Support Vector Machine is higher and gain an ideal classification results, which can establish a basis of real-time BCI application.

<h3>Abstract</h3> <h3>Objective</h3> Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI. <h3>Approach</h3> Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together. <h3>Main Results</h3> LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor. <h3>Significance</h3> These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs. <h3>Suggested PACS</h3> 87.19.L-Neuroscience 87.19.lu motor systems 87.19.rs Movement 87.19.R-Mechanical and electrical properties of tissues and organs 87.85.E-Neural Prosthetics 87.85.Wc Neural engineering 87.85.dd brain-machine interface 87.85.Ng Biological signal processing <h3>IOP Subjects</h3> Medical Physics, Biological Physics

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

In the last few years, Brain Computer Interfaces (BCI) attempted the attention of many researchers. In Motor Imagery (MI)-BCI, central nervous system directly connected to a computer or an automation system. Characteristics of the electromyographic (EEG) signals are utilized in MI-BCI systems. Various techniques have been proposed to extract EEG signal characteristics during recent years. The main objective of this research is to employ an efficient deep learning approach to extract the features of EEG signals using composition of convolutional Neural Network and discrete wavelet transform utilized in the BCI system. The deep learning approach presented in this study has rarely been explored to employ for EEG features extraction. The simulation study indicates that the presented method carry out remarkable accuracy and high performance compared with conventional approaches such as support vector machine and artificial Neural Network methods and give a powerful indicative decision making aid to assist physicians in the treatment of the right and left-hand features for real time motor imagery classification system. Furthermore, the most advantages of employing the proposed method are to eliminate the feature selection level and reducing the processing cost significantly.

In recent years, developments in functional neuroimaging technologies have helped facilitate a clearer understanding of the activation of sites in the brain. This technology is applied to brain-computer interfaces (BCIs). Previous BCIs have primarily used information on the brain activity related to the motor system. In this study, we examined the possibility of controlling the decision-making abilities in the prefrontal cortex and consequently developed a trial BCI. In this experiment, the subject is shown two images; the subject selects one of these images and then the BCI determines the image that the subject selects on the basis of his/her brain information. This system is used to measure brain activity using fNIRS and to acquire data in real time. It preprocesses these data with a low-pass filter; the support vector machine is used as a learning model. Results of the current experiment indicate that the trial BCI developed in this study is not very accurate; however, wireless and lightweight versions of fNIRS are being developed. Results of the current experiment indicate that effective performance of the BCI can be achieved by measurements at specific sites of the brain. These results show that it is possible to develop a BCI for controlling decision-making abilities with lightweight and wireless equipment.

Brain computer interfaces (BCIs) based on motor imagery (MI) play an important role in helping to improve and restore the loss of physical function. However, traditional MI-BCIs are limited to the motion intention of gross limb, which places many restrictions on their applications. This study proposes a hybrid paradigm based on MI and the steady-state somatosensory evoked potential, with the aim of improving the spatial resolution of MI recognition. Twelve subjects participated in this study. They performed MI tasks under MI and hybrid conditions. In the MI condition, subjects only performed MI tasks, whereas, in the hybrid condition, they received an electrical stimulus while performing the same tasks. Under the hybrid condition, subjects were required not to pay extra attention to the electrical stimulation. The MI task included imagining clenching the right hand and lifting the right forearm. The classifier was built using the filter bank common spatial pattern algorithm and a support vector machine, and online experiments were used to verify the recognition of two MI tasks. During the online experiments, all subjects were able to output different commands at a recognition accuracy far higher than the random level. The average classification accuracy of the hybrid condition reached 76.39%, with a maximum value of 88.34%, which was about 11% higher than that of the MI condition. Moreover, based on offline data, the classification performance using the event-related desynchronization (ERD) feature under the hybrid condition did not differ significantly from that under the MI condition, indicating that the introduction of electrical stimulation did not interfere in the separability of ERD. The proposed paradigm improved the efficiency of decoding multiple MI locations within a single limb. Despite the introduction of external stimuli, users could still drive the new system in the same way as MI in traditional MI-BCI.

BackgroundMotor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads.MethodsEleven subjects participated in this study. During the experiment, they were asked to imagine clenching their right hands with two different force loads (30% maximum voluntary contraction (MVC) and 10% MVC). Multi-Common spatial patterns (Multi-CSPs) and support vector machines (SVMs) were used to build the classifier for recognizing three commands corresponding to high load MI, low load MI and relaxed status respectively. EMG were monitored to avoid voluntary muscle activities during the BCI operation. The event-related spectral perturbation (ERSP) method was used to analyse EEG variation during multiple load MI tasks.ResultsAll subjects were able to drive BCI systems using motor imagery of different force loads in online experiments. We achieved an average online accuracy of 70.9%, with the highest accuracy of 83.3%, which was much higher than the chance level (33%). The event-related desynchronization (ERD) phenomenon during high load tasks was significantly higher than it was during low load tasks both in terms of intensity at electrode positions C3 (p < 0.05) and spatial distribution.ConclusionsThis paper demonstrated the feasibility of the proposed MI-BCI paradigm based on multi-force loads on the same limb through online studies. This paradigm could not only enlarge the command set of MI-BCI, but also provide a promising approach to rehabilitate patients with motor disabilities.

The upper limb dysfunction resulted from stroke has a high incidence and is hard to recover, seriously affecting the daily life and life quality of patients. The nerve tissue damage caused by stroke usually leads to the injuries of movement, sensory, cognitive and etc. The effective rehabilitation therapy can promote the rebuilding of the surviving cranial nerves to enhance the function and ability. As a modern intelligent nerve rehabilitation technology, brain-machine interface (BCI) directly builds the exchange channel between the brain and the surrounding environment. BCI extracts the brain's neural activity information, and transfers into the command to drive external equipment. BCI does not rely on the peripheral nerve system and muscle tissue. BCI technology is well suited for neurological rehabilitation training, because the repeated training associated with external equipments and BCI feedback can induce a normal exercise mode, indirectly induced the generation of normal brain signal, and promote brain remodeling, so that improve the patient's athletic ability and exercise control level. In this paper, the research progress of BCI technology for the rehabilitation of the upper limb motor function after stroke was reviewed, including the application, the efficacy and the existing limitations. Key words: Stroke; Rehabilitation of the upper limb motor function; Brain-computer interface