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Table 1 Comparison of different methods for motor decoding

From: Human motor decoding from neural signals: a review

 CorticalPeripheral
 EEGECoGIntra-corticalPeripheral nervesEMG
Decoding siteScalpOn the surface of the brainPenetrated into cortical tissues (e.g. PPC, M1)Peripheral nerves (e.g. ulna, median, radial nerves)Muscles
Types of electrodeDisk electrodesFlexible electrode arrayUtah arrayCuff, intra-neural electrodesSurface electrodes,needle electrodes
Typical spatial resolution [14,169173]5-9 cm<5 mm3-5 μm0.5-2 mm>10 mm
Frequency spectrum0.5-100Hz0-500Hz100Hz-20kHz0.1-10kHz0.1Hz-10kHz
Decodable intentionMovement of different body parts, 2D and 3D direction of movement, different movements of the same limb, individual finger movementMovement of different body parts, different hand gestures, 2D position and velocity of movement, continous finger position2D direction of movement, different hand gesturesDifferent hand gesturesDifferent hand gestures, proportional control of grasps
Signal-to-noise ratioLowMediumHighLowHigh
Signal featureBandpower, ERS/ERDBandpower, LMPSpike firing rate, LFPAction potential firing rateVarious signal features (e.g. RMS, variance, mean absolute value etc.)
InvasivenessLowHighVery highMediumLow
AdvantagesNon-invasive, easily deployableFine-grained and robust feature, mature surgical procedures as part of epilepsy treatmentFine-grained and robust featureLess invasive, potentially contains detailed information about muscle activationsNon-invasive, mature technology, easily deployable
DisadvantagesLow signal-to-noise ratio, high variability of features between sessions, time-consuming to setupInvasive, long-term implantation not commonVery invasive, require implantation surgeryLow signal-to-noise ratioLimited DoF, exessive cross-talk between different channels