Brain-machine interfacing control of whole-body humanoid motion

Magnetoencephalographic evaluation for the myoelectric hand prosthesis with tacit learning system

BACKGROUND

The effect of tacit learning systems (TLSs) on brain plasticity are as of yet unknown. We developed a myoelectric hand prosthesis equipped with a TLS to auto-regulate forearm rotation in response to upper extremity movement patterns.

OBJECTIVE

To evaluate the effects of tacit learning on the central nervous system during a prosthesis control exercise.

METHODS

The experienced prosthetic user performed a series of simple mechanical tasks with the TLS inactivated (the baseline condition) and then with it activated (the enhanced, experimental condition). The process was video recorded. Subsequently, the participant viewed video recordings of each condition (baseline and experimental) during magnetoencephalography and electroencephalography recordings.

RESULTS

Stronger connections between the motor area and other cortical areas were observed, as indicated by a significant increase in coherence values.

CONCLUSIONS

Integration and interoperability may underlie tacit learning and promote motor function-related adaptive neuroplasticity.

Object Extraction in Cluttered Environments via a P300-Based IFCE

One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI) and an improved fuzzy color extractor (IFCE). The induced P300 potential identifies the corresponding region of interest and obtains the target of interest for the IFCE. The classification results not only represent the human mind but also deliver the associated seed pixel and fuzzy parameters to extract the specific objects in which the human is interested. Then, the IFCE is used to extract the corresponding objects. The results show that the IFCE delivers better performance than the BP network or the traditional FCE. The use of a P300-based IFCE provides a reliable solution for assisting a computer in identifying an object of interest within images taken under varying illumination intensities.

Comparative Study of SSVEP- and P300-Based Models for the Telepresence Control of Humanoid Robots

In this paper, we evaluate the control performance of SSVEP (steady-state visual evoked potential)- and P300-based models using Cerebot—a mind-controlled humanoid robot platform. Seven subjects with diverse experience participated in experiments concerning the open-loop and closed-loop control of a humanoid robot via brain signals. The visual stimuli of both the SSVEP- and P300- based models were implemented on a LCD computer monitor with a refresh frequency of 60 Hz. Considering the operation safety, we set the classification accuracy of a model over 90.0% as the most important mandatory for the telepresence control of the humanoid robot. The open-loop experiments demonstrated that the SSVEP model with at most four stimulus targets achieved the average accurate rate about 90%, whereas the P300 model with the six or more stimulus targets under five repetitions per trial was able to achieve the accurate rates over 90.0%. Therefore, the four SSVEP stimuli were used to control four types of robot behavior; while the six P300 stimuli were chosen to control six types of robot behavior. Both of the 4-class SSVEP and 6-class P300 models achieved the average success rates of 90.3% and 91.3%, the average response times of 3.65 s and 6.6 s, and the average information transfer rates (ITR) of 24.7 bits/min 18.8 bits/min, respectively. The closed-loop experiments addressed the telepresence control of the robot; the objective was to cause the robot to walk along a white lane marked in an office environment using live video feedback. Comparative studies reveal that the SSVEP model yielded faster response to the subject’s mental activity with less reliance on channel selection, whereas the P300 model was found to be suitable for more classifiable targets and required less training. To conclude, we discuss the existing SSVEP and P300 models for the control of humanoid robots, including the models proposed in this paper.