806 research outputs found
Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms
Recognizing sEMG (Surface Electromyography) signals belonging to a particular
action (e.g., lateral arm raise) automatically is a challenging task as EMG
signals themselves have a lot of variation even for the same action due to
several factors. To overcome this issue, there should be a proper separation
which indicates similar patterns repetitively for a particular action in raw
signals. A repetitive pattern is not always matched because the same action can
be carried out with different time duration. Thus, a depth sensor (Kinect) was
used for pattern identification where three joint angles were recording
continuously which is clearly separable for a particular action while recording
sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving
Dynamic Time Warping) approach is introduced. This technique is allowed to
retrieve suspected motion of interest from raw signals. MDTW based on DTW
algorithm, but it will be moving through the whole dataset in a pre-defined
manner which is capable of picking up almost all the suspected segments inside
a given dataset an optimal way. Elevated bicep curl and lateral arm raise
movements are taken as motions of interest to show how the proposed technique
can be employed to achieve auto identification and labelling. The full
implementation is available at https://github.com/GPrathap/OpenBCIPytho
A novel onset detection technique for brain?computer interfaces using sound-production related cognitive tasks in simulated-online system
Objective. Self-paced EEG-based BCIs (SP-BCIs) have traditionally been avoided due to two sources of uncertainty: (1) precisely when an intentional command is sent by the brain, i.e., the command onset detection problem, and (2) how different the intentional command is when compared to non-specific (or idle) states. Performance evaluation is also a problem and there are no suitable standard metrics available. In this paper we attempted to tackle these issues. Approach. Self-paced covert sound-production cognitive tasks (i.e., high pitch and siren-like sounds) were used to distinguish between intentional commands (IC) and idle states. The IC states were chosen for their ease of execution and negligible overlap with common cognitive states. Band power and a digital wavelet transform were used for feature extraction, and the Davies?Bouldin index was used for feature selection. Classification was performed using linear discriminant analysis. Main results. Performance was evaluated under offline and simulated-online conditions. For the latter, a performance score called true-false-positive (TFP) rate, ranging from 0 (poor) to 100 (perfect), was created to take into account both classification performance and onset timing errors. Averaging the results from the best performing IC task for all seven participants, an 77.7% true-positive (TP) rate was achieved in offline testing. For simulated-online analysis the best IC average TFP score was 76.67% (87.61% TP rate, 4.05% false-positive rate). Significance. Results were promising when compared to previous IC onset detection studies using motor imagery, in which best TP rates were reported as 72.0% and 79.7%, and which, crucially, did not take timing errors into account. Moreover, based on our literature review, there is no previous covert sound-production onset detection system for spBCIs. Results showed that the proposed onset detection technique and TFP performance metric have good potential for use in SP-BCIs
Evaluating the anticipated outcomes of MRI seizure image from open-source tool- Prototype approach
Epileptic Seizure is an abnormal neuronal exertion in the brain, affecting
nearly 70 million of the world's population (Ngugi et al., 2010). So many
open-source neuroimaging tools are used for metabolism checkups and analysis
purposes. The scope of open-source tools like MATLAB, Slicer 3D, Brain
Suite21a, SPM, and MedCalc are explained in this paper. MATLAB was used by 60%
of the researchers for their image processing and 10% of them use their
proprietary software. More than 30% of the researchers use other open-source
software tools with their processing techniques for the study of magnetic
resonance seizure image
Review of medical data analysis based on spiking neural networks
Medical data mainly includes various types of biomedical signals and medical
images, which can be used by professional doctors to make judgments on
patients' health conditions. However, the interpretation of medical data
requires a lot of human cost and there may be misjudgments, so many scholars
use neural networks and deep learning to classify and study medical data, which
can improve the efficiency and accuracy of doctors and detect diseases early
for early diagnosis, etc. Therefore, it has a wide range of application
prospects. However, traditional neural networks have disadvantages such as high
energy consumption and high latency (slow computation speed). This paper
presents recent research on signal classification and disease diagnosis based
on a third-generation neural network, the spiking neuron network, using medical
data including EEG signals, ECG signals, EMG signals and MRI images. The
advantages and disadvantages of pulsed neural networks compared with
traditional networks are summarized and its development orientation in the
future is prospected
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