Brain-Computer Interface Signal Processing Algorithms: A Computational Cost vs. Accuracy Analysis for Wearable Computers

Ahmadi, Ali; Dehzangi, Omid; Jafari, Roozbeh

doi:10.1109/bsn.2012.19

Cited by 19 publications

(11 citation statements)

References 15 publications

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“…For example, the fall-prediction system proposed in Reference [141] may mistake some special movements for falls and pop the air bag unexpectedly, which will seriously affect usual activities. However, if overemphasizing accuracy while designing a system, it would be inevitable to achieve other target such as low computational cost and low power consumption [152]. Large amounts of work need to be done to balance the relationship between computational cost and accuracy of a system.…”

Section: Applications Of Bsnsmentioning

confidence: 99%

A Survey of Body Sensor Networks

Lai

Quanli

Wei

et al. 2013

Sensors

204

126

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Abstract:The technology of sensor, pervasive computing, and intelligent information processing is widely used in Body Sensor Networks (BSNs), which are a branch of wireless sensor networks (WSNs). BSNs are playing an increasingly important role in the fields of medical treatment, social welfare and sports, and are changing the way humans use computers. Existing surveys have placed emphasis on the concept and architecture of BSNs, signal acquisition, context-aware sensing, and system technology, while this paper will focus on sensor, data fusion, and network communication. And we will introduce the research status of BSNs, the analysis of hotspots, and future development trends, the discussion of major challenges and technical problems facing currently. The typical research projects and practical application of BSNs are introduced as well. BSNs are progressing along the direction of multi-technology integration and intelligence. Although there are still many problems, the future of BSNs is fundamentally promising, profoundly changing the human-machine relationships and improving the quality of people's lives.

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Section: Applications Of Bsnsmentioning

confidence: 99%

A Survey of Body Sensor Networks

Lai

Quanli

Wei

et al. 2013

Sensors

204

126

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“…Feature extraction involves the removal of noisy and artificial data for pure and non-infected data that can be used to develop BCI applications. Different extraction algorithms (also known as conversion transformations) are used to convert original data to a particular vector, such as Independent Component Analysis (ICA) [35], Common Spatial Patterns (CSPs) [27], linear filtering, Fast Fourier Transform (FFT), and Discrete Wavelet Transform (DWT) Select selected vectors by classifying algorithms such as Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs) [30], Neural Networks (NNs), Fuzzy Inference Systems (FISs) [36], and many more. Classification is divided into desired classes.…”

Section: Introductionmentioning

confidence: 99%

Study of Interactive Systems Based on Brain and Computer Interfaces

Ghadiri¹

2019

Preprint

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Scientists have always been looking for ways to create an effective relationship between humans and the machine, so that this relationship is as close as possible to human relationships, since even the most sophisticated machines do not have any particular effect without human intervention. This association results from brain-generated neural responses due to motor activity or cognitive activity. Communication methods include muscle and non-muscle activities that create brain activity or brainwaves and lead to a hardware device to perform a specific task. BCI was originally designed as a communication tool for patients with neuromuscular disorders, but due to recent advances in BCI devices such as passive electrodes, wireless headset, adaptive software, and cost reduction, it has been used to link the rest of the body. The BCI is a bridge between the signals generated by thoughts in our brain and the machines. BCI has been a successful invention in the field of brain imaging, which can be used in a variety of areas, including helping motor activity, vision, hearing, and any damage that the body sustains. The BCI device records brain responses using invasive, semi-invasive and non-invasive methods including Electroencephalography (EEG), Magnetizhenophyllography (MEG), and Magnetic Resonance Imaging (MRI). Brain response using pattern recognition methods to control any translation application. In this article, a review of various techniques for extracting features and classification algorithms has been presented on brain data. A significant comparative analysis of existing BCI techniques is provided.

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“…In this respect, we will consider the CSP technique which seems to be the best spatial filter algorithm for the motor imagery from the effectiveness point of view to extract ERD/ERS effect. The main idea of this technique is to design a pair of spatial filters such that the filtered signal's variance is maximal for one class while minimum for others [1]. For comparison purpose, we use also other variants from the CSP which are SRCSP (Spatially regularized CSP), CCSP1 (Composite Common Spatial Pattern) and DLCSPauto (Diagonal Loading CSP automatic) [13].…”

Section: Overall Brain Computer Interface Descriptionmentioning

confidence: 99%

“…Also the uniformly attenuation of the rejected EEG data lets the diagnosis of the system more complicated to locate the information close to the pass band frequencies. These experiments prove this filter with fewer weights, in general, passes a few activity in the pass bands and more activity in the outside of the band [1]. To filter the usual band motor imagery band, a minimum filter order should be defined as depicted in Figure 4.…”

Section: Eeg Filter Designmentioning

confidence: 99%

“…It looks that digital filters are widely used for EEG signals without deep analysis of their effect on different trials and different data-set leading to a decrease in the system performance for many subjects [1]. In this work, our objective is to find the suitable parameters of the most common used filter for motor imagery domain including: Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) techniques.…”

Section: Introductionmentioning

confidence: 99%

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