Comparing EEG patterns of actual and imaginary wrist movements - a machine learning approach

Our goal is to develop an algorithm for feature extraction and classification to be used in building brain-computer interfaces. In this paper, we present preliminary results for classifying EEG data of imaginary wrist movements. We have developed an algorithm based on the spatio-temporal features of the recorded EEG signals. We discuss the differences between the feature vectors selected for both actual and imaginary wrist movements and compare classification results

A soft wearable robotic exoskeleton for rehabilitation of the frozen shoulder

Adhesive capsulitis (or "frozen shoulder"), is a joint condition resulting in pain and reduction to range of motion (ROM). Contemporary treatments can be effective but currently suffer from a number of drawbacks, including: high cost, lengthy treatment; lack of specificity to each patient; and lack of consistent, repetitive treatment between clinics and therapists [1]. Researchers are thus working to combat these drawbacks by developing rehabilitation exoskeletons. Traditional exoskeletons however aren’t always suitable, being rigid and heavy. Soft robotics may be suitable to overcome all of the above problems, combining the best of robotics (consistency, specificity) while being affordable and wearable through lightweight, compliant materials and design. A direction for the project has been presented, focusing on a pneumatic-based shoulder rehabilitation device, using IMUs as a primary sensor for feedback and control. There will be further detailed research into pneumatics, followed by CAD development to determine an appropriate and effective design. A prototype will be completed by the end of 2018

Heterogeneous real-time multi-channel time-domain feature extraction using parallel sum reduction on GPU

Online BCI has become a fascinating field of research nowadays. One of the main challenges in this field is to reduce the latency caused by the computational complexity of the signal processing algorithms. This issue leads to difficulty in processing real-time data. Usually, a trade-off needs to be considered between the number of input samples and precision of the processing algorithms. In this paper, heterogeneous computing concept is investigated to alleviate the computational complexity occurred in real-time processing. An OpenCL was utilized to implement signal processing algorithms in parallel. Feature extraction methods including band power and statistical moments were selected to examine the power of heterogeneous computing using parallel sum reduction. As a result, varying the number of work-group sizes which is an essential parameter of parallel processing provided dissimilar computing times. Also, running at a higher sampling rate yielded a higher benchmark ratio between sequential and parallel. However, system optimization is still necessary when processing BCI in real time

Evaluation of different signal processing methods in time and frequency domain for brain-computer interface applications

Brain-computer interface (BCI) has been widely introduced in many medical applications. One of the main challenges in BCI is to run the signal processing algorithms in real-time which is challenging and usually comes with high processing unit costs. BCIs based on motor imagery task are introduced for severe neurological diseases especially locked-in patients. A common concept is to detect one’s movement intention and use it to control external devices such as wheelchair or rehabilitation devices. In real-time BCI, running the signal processing algorithms might not always be possible due to the complexity of the algorithms. Moreover, the speed of the affordable computational units is not usually enough for those applications. This study evaluated a range of feature extraction methods which are commonly used for such realtime BCI applications. Electroencephalogram (EEG) and Electrooculogram (EOG) data available through IEEE Brain Initiative repository was used to investigate the performance of different feature extraction methods including template matching, statistical moments, selective bandpower, and fast Fourier transform (FFT) power spectrum. The support vector machine (SVM) was used for classification. The result indicates that there is not a significant difference when utilizing different feature extraction methods in terms of movement prediction although there is a vast difference in the computational time needed to extract these features. The results suggest that computational time could be considered as the primary parameter when choosing the feature extraction methods as there is no significant difference between the results when different features extraction methods are used

Mathematical Description of the Change in Properties of Casuarina Wood Upon Exposure to Gamma Radiation. 1. Changes in the Compressive and Tensile Strength

Casuarina cunninghamiana specimens were exposed to gamma-radiation doses ranging from 104 to 108 rad and tested in compression and tension parallel to grain. The percentage values of the irradiated specimens relative to that of the matched control (Y) were determined. The relationship between (Y) and log gamma radiation dose (X) was represented mathematically by the equation: Y = aXbcx. This equation described the change in compressive and tensile strength very well as was detected from the high correlation coefficients. Generally these properties increased slightly at low levels of radiation, reached a maximum, then decreased gradually thereafter. The reduction in tensile strength was more pronounced than in compressive strength.The threshold dose, i.e., the dose beyond which the properties began to decrease, was calculated. This dose ranged from 3.69 x 106 to 3.76 x 106 rad for compressive strength properties and from 1.51 x 106 to 1.70 x 106 rad for tensile strength properties. This indicated that irradiated casuarina wood had a greater resistance to compression than to tension

Multifaceted value profiles of forest owner categories in South Sweden: The river helge å catchment as a case study

Forest landscapes provide benefits from a wide range of goods, function and intangible values. But what are different forest owner categories\u27 profiles of economic use and non-use values? This study focuses on the complex forest ownership pattern of the River Helge å catchment including the Kristianstad Vattenrike Biosphere Reserve in southern Sweden. We made 89 telephone interviews with informants representing the four main forest owner categories. Our mapping included consumptive and non-consumptive direct use values, indirect use values, and non-use values such as natural and cultural heritage. While the value profiles of non-industrial forest land owners and municipalities included all value categories, the forest companies focused on wood production, and the Swedish Environmental Protection Agency on nature protection. We discuss the challenges of communicating different forest owners\u27 economic value profiles among stakeholders, the need for a broader suite of forest management systems, and fora for collaborative planning. © 2013 The Author(s)

Food, flavouring and feed plant traditions in the Tyrrhenian sector of Basilicata, Italy

BACKGROUND: Research was carried out in the years 2002–2003 into food, flavouring and feed folk traditions of plants in the Tyrrhenian part of the Basilicata region (southern Italy). This area was colonized in ancient times by Greeks. Data was collected through field interviews, especially of farmers. METHODS: Field data were collected through structured interviews. The informants, numbered 49, belonged to families which had strong links with the traditional activities of the area. RESULTS: 61 taxa are cited, belonging to 26 botanical families, amongst which 44 used as food or flavouring and 22 for animal alimentation. Besides 7 taxa are involved in rituals especially connected with agriculture and plant growth. CONCLUSION: The preservation of some rituals especially concerning agricultural plants is noteworthy in the area, together with a certain degree of continuity in food uses. Knowledge and rediscovery of recipes in human and animal diet could represent an economic potential for the area

Spectral analysis of category-specific knowledge in the intact brain

This paper implements spectral analysis of scalp- -EEG recordings during a language naming and visualisation task. The method offers new frontier to explore spatiotemporal features of the organisation of conceptual knowledge in the intact brain. Our findings tallies with results reported in the literature using other techniques such as fMRI. The method introduced in this paper provides new perspective for understanding and possibly diagnosing category specific semantic deficits

Steering a wheelchair by thought

In this paper, we report on preliminary results of research whose aim is to classify EEG signals recorded from a subject whilst controlling a joystick and moving it io different directions. We develop a method based on extracting salient spatio-temporal features from the EEG signals using continuous wavelet transform. We perform principal component analysis on these features as means to assess their usefulness for classification and to reduce the dimensionality of the problem. We use the results from the PCA as inputs to a neural network based classifier. The classification results show that we are able to discriminate between different directions using the selected features. This shows that this approach could be potentially useful in building braincomputer interfaces (BCIs) where a paralysed person could communicate with a wheelchair and steer it to the desired direction using only EEG signals

The effect of the viewing distance of stimulus on SSVEP response for use in brain-computer interfaces : Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on

Most visual stimulators used in steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) to elicit SSVEP response are set close to the BCI system limiting the portability and the applications of the system. Our aim is to develop a portable SSVEP based BCI adaptable to the change of the viewing distance of the stimulator which will allow users to operate the BCI without being restricted to a particular area. This study (1) investigates the impact of the distance between the user and the visual stimulator on the SSVEP response and (2) develops a portable visual stimulator that would adapt to change of distance. The finding suggests that a distance adaptable SSVEP based BCI is achievable by changing the intensity of visual stimulator to correspond to the change of the viewing distance. It is hoped that this work can help SSVEP BCI design and broaden its potential applications and users