Single trial prediction of self-paced reaching directions from EEG signals

Abstract

Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We studied the feasibility of predicting movement directions in self-paced upper limb center-out reaching tasks, i.e., spontaneous movements executed without an external cue that can better reflect natural motor behavior in humans. We reported results of non-invasive electroencephalography (EEG) recorded from mild stroke patients and able-bodied participants. Previous studies have shown that low frequency EEG oscillations are modulated by the intent to move and therefore, can be decoded prior to the movement execution. Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation. For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5 ms before onset using the amplitude of on-going SCPs with above chance level performances between 875 to 437.5 ms prior to onset. The decoding accuracy for the stroke patients was on average 47% with their paretic arms. Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz. Across all the subjects, including stroke subjects, the best selected features were obtained mostly from the fronto-parietal regions, hence consistent with previous neurophysiological studies on arm reaching tasks. In summary, we concluded that SCPs allow the possibility of single trial decoding of reaching directions at least 312.5 ms before onset of reach.

EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

“…The majority of BCI studies were focused on techniques that do not respond to wishes and intents directly, such as motor imagery and responses to stimuli. Recently, slow premotor potentials were employed for developing neurorehabilitation BCIs (Niazi et al, 2011; Ibáñez et al, 2014; Lew et al, 2014; Xu et al, 2014; Jiang et al, 2015; Shakeel et al, 2015). But probably only the works by Zander's group (Ihme and Zander, 2011; Protzak et al, 2013) addressed the direct conversion of intentions into actions in line with the elegant approach by Gray Walter, now also enhanced with gaze capabilities.…”

Evaluation of human electroencephalogram change for sensory effects of fragrance

“…Moreover, all participants responded that this test method is novel and easy for both the participants and tester. Therefore, the disadvantages of the old EEG test (lengthy test time and difficulty in wearing equipment and test preparation) were overcome by this new method. From the participants’ standpoint, the advantages of the new test method affected the results positively, as the fragrance's definite effect on the feeling can be distinguished easily.…”

“…Typically, the delta wave is known to be affected by eye movements, and the gamma wave is affected by fine body movements or the surrounding electronic devices. The definition of the theta wave result is highly controversial, and the flash of the test environment influences the test results, and provides the test run time and end results . Hence, only the alpha, beta, and α / β parameters were analyzed eventually, and delta, theta, and gamma were excluded in this study.…”

“…EEG is a universally used device for brainwave measurements and can verify the brain activity using numerous brainwaves. The measurement is performed based on the International 10-20 system, and the wavelengths are classified into delta (0.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma . Through the association between brainwave and fragrance, the effects of fragrance on mood and preference can be verified.…”

Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG Signals

“…Discrete decoding (neural classification) of intent from EEG signals can be considered as a pattern recognition problem, and advanced machine learning techniques are needed to accurately translate the brain electrical activities to meaningful control commands. Many machine learning methods [e.g., linear discriminant analysis (LDA), support vector machine (SVM), Bayesian classifiers] have been applied for classifying EEG signals in different BMI applications (Kilicarslan et al, 2013; Niazi et al, 2013; Leamy et al, 2014; Lew et al, 2014; Hortal et al, 2015; Jiang et al, 2015). However, most of them serve as a “black box” in that we do not know how the brain activity changes during long-term BMI use nor how the brain regions contribute to the classification process while people perform different tasks.…”