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International Conference of the IEEE Engineering in Medicine and Biology Society

About: International Conference of the IEEE Engineering in Medicine and Biology Society is an academic conference. The conference publishes majorly in the area(s): Image segmentation & Population. Over the lifetime, 48771 publication(s) have been published by the conference receiving 557630 citation(s).

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48,771 results found


Open accessJournal ArticleDOI: 10.1109/86.895946
01 Dec 2000-
Abstract: The development of an electroencephalograph (EEG)-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e.g., associated with imaginary movement. One-sided hand movement imagination results in EEG changes located at contra- and ipsilateral central areas. The authors demonstrate that spatial filters for multichannel EEG effectively extract discriminatory information from two populations of single-trial EEG, recorded during left- and right-hand movement imagery. The best classification results for three subjects are 90.8%, 92.7%, and 99.7%. The spatial filters are estimated from a set of data by the method of common spatial patterns and reflect the specific activation of cortical areas. The method performs a weighting of the electrodes according to their importance for the classification task. The high recognition rates and computational simplicity make it a promising method for an EEG-based brain-computer interface.

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Topics: Electroencephalography (52%)

1,963 Citations


Open accessJournal ArticleDOI: 10.1109/TRE.2000.847807
01 Jun 2000-
Abstract: Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.

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Topics: Central element (50%)

1,899 Citations


Open accessJournal ArticleDOI: 10.1109/86.662623
01 Mar 1998-
Abstract: The authors' goal is to apply robotics and automation technology to assist, enhance, quantify, and document neurorehabilitation. This paper reviews a clinical trial involving 20 stroke patients with a prototype robot-aided rehabilitation facility developed at the Massachusetts Institute of Technology, Cambridge, (MIT) and tested at Burke Rehabilitation Hospital, White Plains, NY. It also presents the authors' approach to analyze kinematic data collected in the robot-aided assessment procedure. In particular, they present evidence (1) that robot-aided therapy does not have adverse effects, (2) that patients tolerate the procedure, and (3) that peripheral manipulation of the impaired limb may influence brain recovery. These results are based on standard clinical assessment procedures. The authors also present one approach using kinematic data in a robot-aided assessment procedure.

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1,284 Citations


Journal ArticleDOI: 10.1109/TITB.2005.856864
01 Jan 2006-
Abstract: The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence

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Topics: Functional ability (54%)

1,278 Citations


Open accessJournal ArticleDOI: 10.1109/86.847808
01 Jun 2000-
Abstract: Describes a study designed to assess a brain-computer interface (BCI), originally described by Farwell and Donchin in 1988. The system utilizes the fact that the rare events in the oddball paradigm elicit the P300 component of the event-related potential (ERP). The BCI presents the user with a matrix of 6 by 6 cells, each containing one letter of the alphabet. The user focuses attention on the cell containing the letter to be communicated while the rows and the columns of the matrix are intensified. Each intensification is an event in the oddball sequence, the row and the column containing the attended cell are "rare" items and, therefore, only these events elicit a P300. The computer thus detects the transmitted character by determining which row and which column elicited the P300. The authors report an assessment, using a bootstrapping approach, which indicates that an off line version of the system can communicate at the rate of 7.8 characters a minute and achieve 80% accuracy. The system's performance in real time was also assessed. The authors' data indicate that a P300-based BCI is feasible and practical. However, these conclusions are based on tests using healthy individuals.

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Topics: Oddball paradigm (52%), Interface (computing) (50%)

1,188 Citations


Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
2021240
20201,791
20191,916
20181,751
20171,375
20161,726

Top Attributes

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Conference's top 5 most impactful authors

Nitish V. Thakor

200 papers, 2.1K citations

Nigel H. Lovell

200 papers, 3.6K citations

Hung T. Nguyen

125 papers, 1.6K citations

Dimitrios I. Fotiadis

101 papers, 1.7K citations

Bin He

98 papers, 1.1K citations

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