Predicting Reaching Targets from Human EEG

Hammon, Paul S.; Makeig, Scott; Poizner, Howard; Todorov, Emanuel; de, Virginia R.

doi:10.1109/msp.2008.4408443

Cited by 97 publications

(64 citation statements)

References 16 publications

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“…Non-invasive BMIs measure synchronous activities of thousands of cortical neurons, such as electroencephalogram (EEG) activities, via electrodes positioned on the scalp [16][17][18][19]. By contrast, invasive BMIs typically use microelectrodes implanted in the cortex to record extracellular activities of neurons [20][21][22][23].…”

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confidence: 99%

Development of an invasive brain-machine interface with a monkey model

Zhang

Hao

et al. 2012

Chin. Sci. Bull.

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Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey's arm or hand in many studies. However, there are few studies on decoding the wrist movement from neural activities in center-out paradigm. The present study developed an invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex. The monkey was trained to perform a two-dimensional forelimb wrist movement paradigm where neural activities and movement signals were simultaneous recorded. Results showed that neuronal firing rates highly correlated with forelimb wrist movement; > 70% (105/149) neurons exhibited specific firing changes during movement and > 36% (54/149) neurons were used to discriminate directional pairs. The neuronal firing rates were also used to predict the wrist moving directions and continuous trajectories of the forelimb wrist. The four directions could be classified with 96% accuracy using a support vector machine, and the correlation coefficients of trajectory prediction using a general regression neural network were above 0.8 for both horizontal and vertical directions. Results showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information.

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mentioning

confidence: 99%

Development of an invasive brain-machine interface with a monkey model

Zhang

Hao

et al. 2012

Chin. Sci. Bull.

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“…Then the structure of the transformed joint data may be explored using data-information based machine learning methods (Baker et al, 2005), and the EEG brain sources imaged using statistical inverse imaging methods (Michel et al, 2004;Wipf et al, 2007). Simple averaging of power spectral changes in independent component time courses unmixed from preliminary MoBI experiments reveal spectral shifts with distinct temporal relationships to particular phases of simple reaching movements (Hammon et al, 2008;Makeig et al, 2007) (Fig. 2b, d).…”

Section: Identifying Links Between Behavior and Eeg Dynamicsmentioning

confidence: 90%

Linking brain, mind, and behavior

Makeig

2008

International Journal of Psychophysiology

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“…The ability to determine which spectral bands contribute to discrimination and the extent of their contribution would provide valuable insight into the biological and chemical basis of the differences observed between tissue types. Prior work has utilized the SMLR weights in order to evaluate the relative importance that individual features contribute to discrimination [41]. However, since SMLR promotes sparsity and weights are calculated during individual cross-validation operations, it is possible for a spectral feature to be substantially weighted but not used consistently across the entire data set.…”

Section: Discussionmentioning

confidence: 99%

Discrimination of liver malignancies with 1064 nm dispersive Raman spectroscopy

Pence

Patil

Lieber³

et al. 2015

Biomed. Opt. Express

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Abstract:Raman spectroscopy has been widely demonstrated for tissue characterization and disease discrimination, however current implementations with either 785 or 830 nm near-infrared (NIR) excitation have been ineffectual in tissues with intense autofluorescence such as the liver. Here we report the use of a dispersive 1064 nm Raman system using a low-noise Indium-Gallium-Arsenide (InGaAs) array to discriminate highly autofluorescent bulk tissue ex vivo specimens from healthy liver, adenocarcinoma, and hepatocellular carcinoma (N = 5 per group). The resulting spectra have been combined with a multivariate discrimination algorithm, sparse multinomial logistic regression (SMLR), to predict class membership of healthy and diseased tissues, and spectral bands selected for robust classification have been extracted. A quantitative metric called feature importance is defined based on classification outputs and is used to guide the association of spectral features with biological indicators of healthy and diseased liver tissue. Spectral bands with high feature importance for healthy and liver tumor specimens include retinol, heme, biliverdin, or quinones (1595 cm −1 ); lactic acid (838 cm −1 ); collagen (873 cm −1 ); and nucleic acids (1485 cm −1 ). Classification performance in both binary (normal versus tumor, 100% sensitivity and 89% specificity) and three-group cases (classification accuracy: normal 89%, adenocarcinoma 74%, hepatocellular carcinoma 64%) indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing Raman techniques during surgical guidance in liver resection.

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Predicting Reaching Targets from Human EEG

Cited by 97 publications

References 16 publications

Development of an invasive brain-machine interface with a monkey model

Development of an invasive brain-machine interface with a monkey model

Linking brain, mind, and behavior

Discrimination of liver malignancies with 1064 nm dispersive Raman spectroscopy

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