Brain-Machine Interface (BMI) uses electrophysiological measures of brain function to enable communication between the brain and external devices, such as computers or mechanical actuators. Previous studies using intracortical BMI’s have demonstrated the decoding of neuronal ensemble activity in the dorsal pre-motor cortex (PMd) [1
] primary motor cortex (M1) [3
], and posterior parietal cortex (PPC) [7
] for the purposes of deriving a variety of cortical control signals.
Previous work using signals from the rat motor cortex has shown 1-D control of a robotic arm using a population of 32 M1 neurons to predict hand trajectory [3
]. Later work shows how simultaneous activity from the PMd, M1, and PPC areas of non-human primates could accurately predict 3-D arm movement trajectories [9
]. Additional research in non-human primates has shown that activity from even relatively few neurons in M1 can reliably decode movement of a cursor on a computer screen [5
]. These results have led to breakthroughs in the translation of direct cortical control strategies from animal trials to humans. Just recently, a tetraplegic human was able to control a computer cursor to open e-mail, operate a television, and open and close a prosthetic hand using neuronal ensemble activity from a microelectrode array [5
In general, these experiments have demonstrated the robust coding capacity of neural populations and have opened up the possibility of a BMI for direct neural control of a prosthetic limb and the restoration of motor control for amputees, paralyzed individuals, and those with degenerative muscular diseases. These efforts have focused largely on controlling a computer cursor [5
], predicting movement intent [1
], and decoding hand and arm trajectory [2
], however, and current neural control strategies do not allow for dexterous
neuroprosthetic control of actions such as individuated and combined finger movements.
Prior studies of motor cortical activities in primates by Schieber et al [11
] and Georgopoulos et al [15
] have shown that there are indeed neurons in the primary motor cortex that code for specific finger movements, and which discharge during movements of several fingers. Their results also suggest that neuronal populations active with movements of different fingers overlap extensively in their spatial locations in the motor cortex [13
]. Although there are neurons that fire maximally for a specific movement type these neurons are not localized to a specific anatomic region, but rather are distributed throughout the hand region [14
]. More recent MRI studies with humans [17
] also show that while there are regions of the primary motor cortex active with different finger movements, there is also an overlap of finger representation in the motor homunculus. The lack of A strict somatotopic organization of the M1 hand area suggests that a given subpopulation of neurons in this region should contain sufficient information to encode for individuated and combined movements of the fingers and wrist.
Original work using population vectors [15
], which combine the weighted contributions from each neuron along a preferred direction, found that 75% of motor cortical neurons related to finger movements were tuned to specific directions in a 3D instructed hand movement space. Although population vectors were fairly good predictors of the direction of instructed finger movements, this approach only yielded 60–70% decoding accuracy in recent work by Pouget et al [19
] on decoding finger movements. However, using non-linear classification schemes such as logistic regression and softmax, they demonstrated close to 100% decoding accuracy of individuated finger movements using only 20–30 neurons. Extended to combined finger movements, they obtained approximately 90% decoding accuracy, albeit using a substantially larger neuronal population.
Although it has been shown that dexterous movements can indeed be decoded, earlier work in this field has relied on a priori
knowledge of the movement events [15
]. BMI control of a neuroprosthesis, on the other hand, requires streaming of neuronal data in real-time where cues indicating the onset of movement are not known. The goal of this study was to demonstrate, for the first time, the feasibility of a BMI for dexterous control of individual fingers and the wrist of a multi-fingered prosthetic hand by decoding both movement intent and movement type. The final decoded output then was used to actuate a multi-fingered robotic hand in real-time for a pre-planned task.