Identification of movement-related cortical potentials with optimized spatial filtering and principal component analysis

Boye, Andreas Trøllund; Kristiansen, Ulrik Qvist; Billinger, Martin; Nascimento, Omar Feix do; Farina, Dario

doi:10.1016/j.bspc.2008.05.001

Cited by 40 publications

(22 citation statements)

References 15 publications

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“…In the current study, the signal was divided into epochs (based on a priori knowledge), and background EEG and movement epochs were classified contrary to online systems or simulated online systems [21,[31][32][33]45]. When comparing this classification-based approach to discriminate between movement and background EEG, similar performance is obtained for other studies where the detection performance was 80-90% [3,6,23]. The spectral features that were most discriminative were in the MRCP frequency range (0-5 Hz), but also 5-15 Hz where sensorimotor rhythms are modulated by movement preparation [35].…”

Section: Detectionsupporting

confidence: 52%

“…Out of those that survive the initial injury, up to 85% are initially left with motor disabilities such as a hemiplegic arm. Despite the rehabilitation efforts, 55-75% of the patients remain with some disability [3][4][5][6] months after the injury [10]; therefore, there is an incitement to optimize the rehabilitation process, e.g. by introducing new interventions to add to the current therapies, to maximize the outcome of the rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Detectionsupporting

confidence: 52%

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

View full text Add to dashboard Cite

show abstract

“…This ANN-based model allows a seamless incorporation of neuromuscular activity, detected from paralyzed individuals, to adaptively predict their altered gait patterns, which can be employed to provide closed-loop feedback information for neural prostheses. Support vector machine implementations are used in [32], [33], [34]. The results indicate that the proposed algorithms are promising for future use of rehabilitative BCI applications in neurologically impaired patients.…”

Section: Considerations On Eeg Signals and Signal Processing Methods mentioning

confidence: 97%

Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

Dzițac

Vesselényi

Ţarcă

2011

INT J COMPUT COMMUN

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A Brain-Computer Interface uses measurements of scalp electric potential (electroencephalography -EEG) reflecting brain activity, to communicate with external devices. Recent developments in electronics and computer sciences have enabled applications that may help users with disabilities and also to develop new types of Human Machine Interfaces. By producing modifications in their brain potential activity, the users can perform control of different devices. In order to perform actions, this EEG signals must be processed with proper algorithms. Our approach is based on a fuzzy inference system used to produce sharp control states from noisy EEG data.

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“…This is particularly important for online experiments. At this stage, it is also possible to include optional processing steps, such as spatial filtering, independent component analysis etc., which would either enhance the power of the MRCP (Boye et al 2008), or remove signals corrupted by artifacts, EOG and EMG (Jiang et al 2014).…”

Section: Signal Processing Of Mrcp For Detection Of Movement Intentionsmentioning

confidence: 99%

Brain-Computer Interface Research

Guger¹,

Müller-Putz²,

Allison³

2014

SpringerBriefs in Electrical and Computer Engineering

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Identification of movement-related cortical potentials with optimized spatial filtering and principal component analysis

Cited by 40 publications

References 15 publications

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

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

Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

Brain-Computer Interface Research

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