Reconstructing hand kinematics during reach to grasp movements from electroencephalographic signals

Agashe, Harshavardhan A.; Contreras-Vidal, José L.

doi:10.1109/iembs.2011.6091389

Cited by 38 publications

(37 citation statements)

References 20 publications

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“…More recently a few studies have reported a bandpass filter in the range [0. 1 1] Hz [45,46,15]. The appropriate choice of spectral range for the single trial analysis of SCPs has been elusive.…”

Section: Slow Cortical Potentialsmentioning

confidence: 99%

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Single trial analysis of slow cortical potentials: a study on anticipation related potentials

2013

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Objective. Abundant literature suggests the use of slow cortical potentials (SCPs) in a wide spectrum of basic and applied neuroscience areas. Due to their low signal to noise ratio, these potentials are often studied using grand-average analysis, which conceals trial-to-trial information. Moreover, most of the single trial analysis methods in the literature are based on classical electroencephalogram (EEG) features ([1-30] Hz) and are likely to be unsuitable for SCPs that have different signal properties (such as having the signal's spectral content in the range [0.2-0.7] Hz). In this paper we provide insights into the selection of appropriate parameters for spectral and spatial filtering. Approach. We study anticipation related SCPs recorded using a web-browser application protocol and a full-band EEG (FbEEG) setup from 11 subjects on two different days. Main results. We first highlight the role of a bandpass with [0.1-1.0] Hz in comparison with common practices (e.g., either with full dc, just a lowpass, or with a minimal highpass cut-off around 0.05 Hz). Secondly, we suggest that a combination of spatial-smoothing filter and common average reference (CAR) is more suitable than the spatial filters often reported in the literature (e.g., re-referencing to an electrode, Laplacian or CAR alone). Thirdly, with the help of these preprocessing steps, we demonstrate the generalization capabilities of linear classifiers across several days (AUC of 0.88 ± 0.05 on average with a minimum of 0.81 ± 0.03 and a maximum of 0.97 ± 0.01). We also report the possibility of further improvements using a Bayesian fusion technique applied to electrode-specific classifiers. Significance. We believe the suggested spatial and spectral preprocessing methods are advantageous for grand-average and single trial analysis of SCPs obtained from EEG, MEG as well as for electrocorticogram. The use of these methods will impact basic neurophysiological studies as well as the use of SCPs in the design of neuroprosthetics.

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Section: Slow Cortical Potentialsmentioning

confidence: 99%

“…Hence, they may also be used for decoding hand movement trajectories [14,15]. Interestingly, SCPs have also been used to assess the role of the premotor cortex in distinguishing self-initiated and cue-initiated movement preparation [16].…”

Section: Introductionmentioning

confidence: 99%

Single trial analysis of slow cortical potentials: a study on anticipation related potentials

2013

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“…Recently, it was demonstrated that MRCPs associated with movements performed with different levels of force and speed of the same body part (wrist and foot movements) could be decoded from the EEG using only information prior to the onset of the movement [13,14,21]. Also, different movement types have been classified such as hand grasping, opening and reaching [1,2,4], movement direction and kinematics (see [19] for a recent review), wrist movements [12,[40][41][42], shoulder and elbow movements [8,[47][48][49] and finger movements [26,27,44].…”

Section: Introductionmentioning

confidence: 99%

Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation

Jochumsen

Niazi

Dremstrup

et al. 2015

Med Biol Eng Comput

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Brain-computer interfaces can be used for motor substitution and recovery; therefore, detection and classification of movement intention is crucial for optimal control. In this study, palmar, lateral and pinch grasps were differentiated from the idle state and classified from single-trial EEG using only information prior the movement onset. Fourteen healthy subjects performed the three grasps 100 times while EEG was recorded from 25 electrodes. Temporal and spectral features were extracted from each electrode, and feature reduction was performed using sequential forward selection (SFS) and principal component analysis (PCA).The detection problem was investigated as the ability to discriminate between movement preparation and the idle state. Furthermore, all task pairs and the three movements together were classified. The best detection performance across movements (79±8%) was obtained by combining temporal and spectral features. The best movement-movement discrimination was obtained using spectral features; 76±9% (2-class) and 63±10% (3-class). For movement detection and discrimination, the performance was similar across grasp types and task pairs; SFS outperformed PCA. The results show it is feasible to detect different grasps and classify the distinct movements using only information prior to the movement onset; which may enable brain-computer interface-based neurorehabilitation of upper limb function through Hebbian learning mechanisms.

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“…Finger kinematics for a single dof were specified at an 8-bit resolution, with 0 and 255 corresponding to open and fully flexed positions, respectively. Nominally, two synergies of grasping based on PC analysis of the joint angles were identified based on previous work (Agashe and Contreras-Vidal, 2011;Agashe et al, 2015;Santello et al, 1998) corresponding to the correlated movement of the flexion-extension across all fingers and the thumb (PC1), and the thumb rotation (PC2).…”

Section: Figmentioning

confidence: 99%

Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees

Agashe

Paek

Contreras-Vidal

2016

Progress in Brain Research

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Reconstructing hand kinematics during reach to grasp movements from electroencephalographic signals

Cited by 38 publications

References 20 publications

Single trial analysis of slow cortical potentials: a study on anticipation related potentials

Single trial analysis of slow cortical potentials: a study on anticipation related potentials

Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation

Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees

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