EEG-Based Classification of Imagined Arm Trajectories

Ofner, Patrick; Müller-Putz, Gernot

doi:10.1007/978-3-319-08072-7_88

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…In addition to Bradberry et al's work in noninvasive EEG technology, Ofner et al [64] studied the continuous and natural imaginary movements of the right hand in a 2D plane. They estimated the imagined continuous velocities from EEG signals.…”

Section: Imagined Body Kinematics Paradigmsmentioning

confidence: 99%

A comprehensive review of EEG-based brain–computer interface paradigms

et al. 2019

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Advances in brain science and computer technology in the past decade have led to exciting developments in brain–computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

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Section: Imagined Body Kinematics Paradigmsmentioning

confidence: 99%

A comprehensive review of EEG-based brain–computer interface paradigms

et al. 2019

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“…However, only 1 target was presented on a screen in each trial, and [39] showed that a random cursor also hits this target after some time, and they reached similar decoding results. Thus, we designed a new experiment where subjects imagined arm movements and decoded them (preliminary results have been shown in [40] [41]). However, there is one substantial issue.…”

Section: Introductionmentioning

confidence: 99%

Using a Noninvasive Decoding Method to Classify Rhythmic Movement Imaginations of the Arm in Two Planes

Ofner

Müller-Putz

2015

IEEE Trans. Biomed. Eng.

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A brain-computer interface (BCI) can help to overcome movement deficits in persons with spinal-cord injury. Ideally, such a BCI detects detailed movement imaginations, i.e., trajectories, and transforms them into a control signal for a neuroprosthesis or a robotic arm restoring movement. Robotic arms have already been controlled successfully by means of invasive recording techniques, and executed movements have been reconstructed using noninvasive decoding techniques. However, it is unclear if detailed imagined movements can be decoded noninvasively using electroencephalography (EEG). We made progress toward imagined movement decoding and successfully classified horizontal and vertical imagined rhythmic movements of the right arm in healthy subjects using EEG. Notably, we used an experimental design which avoided muscle and eye movements to prevent classification results being affected. To classify imagined movements of the same limb, we decoded the movement trajectories and correlated them with assumed movement trajectories (horizontal and vertical). We then assigned the decoded movements to the assumed movements with the higher correlation. To train the decoder, we applied partial least squares, which allowed us to interpret the classifier weights although channels were highly correlated. To conclude, we showed the classification of imagined movements of one limb in two different movement planes in seven out of nine subjects. Furthermore, we found a strong involvement of the supplementary motor area. Finally, as our classifier was based on the decoding approach, we indirectly showed the decoding of imagined movements.

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