Dudes, time to put on y’alls thinking hats. Those that will go on a train or tram don’t forget the aluminum foil hat ;)<\/p>\n
Reconstructing Three-Dimensional Hand Movements from Noninvasive Trent J. Bradberry<\/a>,1 Rodolphe J. Gentili,2,3 and Jose\u017d L. Contreras-Vidal1,2,3<\/p>\n 1 Fischell Department of Bioengineering, [text below] It is generally thought that the signal-to-noise ratio, the bandwidth, and the information content of neural data acquired via noninvasive scalp electroencephalography (EEG) are The Journal of Neuroscience<\/a>, March 3, 2010 \u0095 30(9):3432\u00963437<\/p>\n\n
\nElectroencephalographic Signals<\/strong><\/p>\n
\n2 Department of Kinesiology, and
\n3 Graduate Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742<\/p>\n
\n<\/p>\n
\ninsufficient to extract detailed informationaboutnatural, multijointmovementsof theupperlimb. Here,wechallenge thisassumptionbycontinuouslydecodingthree-dimensional
\n(3D) hand velocity from neural data acquired from the scalp with 55-channel EEG during a 3D center-out reaching task. To preserve ecological validity, five subjects self-initiated
\nreaches and self-selected targets. Eye movements were controlled so they would not confound the interpretation of the results. With only 34 sensors, the correlation between
\nmeasured and reconstructed velocity profiles compared reasonably well to that reported by studies that decoded hand kinematics from neural activity acquired intracranially.We
\nsubsequently examined the individual contributions of EEG sensors to decoding to find substantial involvement of scalp areas over the sensorimotor cortex contralateral to the
\nreaching hand. Using standardized low-resolution brain electromagnetic tomography (sLORETA), we identified distributed current density sources related to hand velocity in the
\ncontralateral precentral gyrus, postcentral gyrus, and inferior parietal lobule. Furthermore, we discovered that movement variability negatively correlated with decoding accuracy, a
\nfinding to consider during the development of brain\u0096computer interface systems. Overall, the ability to continuously decode 3D hand velocity from EEG during natural, center-out
\nreaching holds promise for the furtherance of noninvasive neuromotor prostheses for movement-impaired individuals.<\/p>\n