The performance of neural decoders can degrade over time due to nonstationarities in the relationship between neuronal activity and behavior. In this case, brain-machine interfaces (BMI) require adaptation of their decoders to maintain high performance across time. One way to achieve this is by use of periodical calibration phases, during which the BMI system (or an external human demonstrator) instructs the user to perform certain movements or behaviors. This approach has two disadvantages: (i) calibration phases interrupt the autonomous operation of the BMI and (ii) between two calibration phases the BMI performance might not be stable but continuously decrease. A better alternative would be that the BMI decoder is able to continuously adapt in an unsupervised manner during autonomous BMI operation, i.e. without knowing the movement intentions of the user. In the present article, we present an efficient method for such unsupervised training of BMI systems for continuous movement control. The proposed method utilizes a cost function derived from neuronal recordings, which guides a learning algorithm to evaluate the decoding parameters. We verify the performance of our adaptive method by simulating a BMI user with an optimal feedback control model and its interaction with our adaptive BMI decoder. The simulation results show that the cost function and the algorithm yield fast and precise trajectories towards targets at random orientations on a 2-dimensional computer screen. For initially unknown and non-stationary tuning parameters, our unsupervised method is still able to generate precise trajectories and to keep its performance stable in the long term. The algorithm can optionally work also with neuronal error signals instead or in conjunction with the proposed unsupervised adaptation.
In earlier work, BMI research has already addressed online adaptivity issue. For instance, @cite_28 has proposed a BMI system, where individual neuron's directional tuning changes are tracked with online adaptive linear filters. Wolpaw and McFarland have shown that intended @math -dimensional cursor movements can be estimated from EEG recordings @cite_63 . In that study, they employed Least Mean Squares (LMS) algorithm to update the parameters of a linear filter after each trial. Later, has also shown that a similar method can be used to decode @math -dimensional movements from EEG recordings @cite_56 . have proposed adaptive versions of Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis for cue-based discrete choice BMI-tasks @cite_64 @cite_52 @cite_31 . These works employ supervised learning algorithms, i.e. they necessitate that the decoder knows the target of the movement or the choice in advance and adapts the decoding parameters. In other words, the employed methods know and make use of the label of the recorded neural activity.
@cite_64 @cite_28 @cite_52 @cite_56 @cite_63 @cite_31
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A viable fully on-line adaptive brain computer interface (BCI) is introduced. On-line experiments with nine naive and able-bodied subjects were carried out using a continuously adaptive BCI system. The data were analyzed and the viability of the system was studied. The BCI was based on motor imagery, the feature extraction was performed with an adaptive autoregressive model and the classifier used was an adaptive quadratic discriminant analysis. The classifier was on-line updated by an adaptive estimation of the information matrix (ADIM). The system was also able to provide continuous feedback to the subject. The success of the feedback was studied analyzing the error rate and mutual information of each session and this analysis showed a clear improvement of the subject's control of the BCI from session to session. Control signals for an object are developed from the neuron-originating electrical impulses detected by arrays of electrodes chronically implanted in a subject's cerebral cortex at the pre-motor and motor locations known to have association with arm movements. Taking as an input the firing rate of the sensed neurons or neuron groupings that affect a particular electrode, a coadaptive algorithm is used. In a closed-loop environment, where the animal subject can view its results, weighting factors in the algorithm are modified over a series of tests to emphasize cortical electrical impulses that result in movement of the object as desired. At the same time, the animal subject learns and modifies its cortical electrical activity to achieve movement of the object as desired. In one specific embodiment, the object moved was a cursor portrayed as a sphere in a virtual reality display. Target objects were presented to the subject, who then proceeded to move the cursor to the target and receive a reward. In a noncoadaptive use of the algorithm as previously modified by a co-adaptation, unlearned targets were presented in the virtual reality system and the subject moved the cursor to these targets. In another embodiment, a robot arm was controlled by an animal subject. Brain–computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses. Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains.