We developed a novel movement-imagery-based brain-computer interface (BCI) for untrained subjects without employing machine learning techniques. The development of BCI consisted of several steps. First, spline Laplacian analysis was performed. Next, time-frequency analysis was applied to determine the optimal frequency range and latencies of the electroencephalograms (EEGs). Finally, trials were classified as right or left based on β-band event-related synchronization using the cumulative distribution function of pretrigger EEG noise. To test the performance of the BCI, EEGs during the execution and imagination of right/left wrist-bending movements were measured from 63 locations over the entire scalp using eight healthy subjects. The highest classification accuracies were 84.4% and 77.8% for real movements and their imageries, respectively. The accuracy is significantly higher than that of previously reported machine-learning-based BCIs in the movement imagery task (paired t-test, p < 0.05). It has also been demonstrated that the highest accuracy was achieved even though subjects had never participated in movement imageries.

In this paper, we propose improved methods of liquid-phase detection of biological targets utilizing magnetic markers and a high-critical-temperature superconducting quantum interference device (SQUID). For liquid-phase detection, the bound and unbound (free) markers are magnetically distinguished by using Brownian relaxation of free markers. Although a signal from the free markers is zero in an ideal case, it exists in a real sample on account of the aggregation and precipitation of free markers. This signal is called a blank signal, and it degrades the sensitivity of target detection. To solve this problem, we propose improved detection methods. First, we introduce a reaction field, Bre, during the binding reaction between the markers and targets. We additionally introduce a dispersion process after magnetization of the bound markers. Using these methods, we can obtain a strong signal from the bound markers without increasing the aggregation of the free markers. Next, we introduce a field-reversal method in the measurement procedure to differentiate the signal from the markers in suspension from that of the precipitated markers. Using this procedure, we can eliminate the signal from the precipitated markers. Then, we detect biotin molecules by using these methods. In an experiment, the biotins were immobilized on the surfaces of large polymer beads with diameters of 3.3 µm. They were detected with streptavidin-conjugated magnetic markers. The minimum detectable molecular number concentration was 1.8×10-19 mol/ml, which indicates the high sensitivity of the proposed method.