Classification error correction: A case study in brain-computer interfacing

Classification techniques are useful for processing complex signals into labels with semantic value. For example, they can be used to interpret brain signals generated by humans corresponding to a finite set of commands for a physical device. The classifier, however, may interpret the signal as a command that is different from the intended one. This error in classification leads to poor performance in tasks where the class labels are used to learn some information or to control a physical device. We propose a computationally efficient algorithm to identify which class labels may be misclassified out of a sequence of class labels, when these labels are used in a given learning or control task. The algorithm is based on inference methods using Markov random fields. We apply the algorithm to goal-learning and tracking using brain-computer interfacing (BCI), in which signals from the brain are commonly processed using classification techniques. We demonstrate that the proposed algorithm reduces the time taken to identify the goal state in control experiments.

Authors

• Hasan A. Poonawala
• Mohammed Alshiekh
• Scott Niekum
• Ufuk Topcu

Keywords

• Electroencephalography
• Inference algorithms
• Random variables
• Markov processes
• Brain-computer interfaces
• Noise measurement
• Classification Error
• Task Performance
• Efficient Algorithm
• Class Labels
• Finite Set
• Goal State
• Markov Random Field
• Poor Task Performance
• Time Step
• State Space
• Confusion Matrix
• Energy Function
• EEG Signals
• Discrete Set
• Truth Labels
• Inference Algorithm
• Classification Test
• Correct Label
• Optimal Action
• Steady-state Visual Evoked Potential
• True Class Label
• Energy Minimization Problem
• Subset Of Nodes
• Potential Energy Function
• Adjacent States
• Human Users
• Classification Output
• Maximum A Posteriori