Dual-state adaptive spatial filtering is an approach to source reconstruction that can estimate neuromagnetic activities with high spatial resolution by forming a linear combination of sensors that suppresses the signals from environmental noise or other brain areas without attenuating the power from the target voxel, and which is optimized for time-frequency source reconstructions from MEG/EEG data [46-49]. The adaptive spatial filtering using a single spherical volume conductor model based on the individual MR image was obtained as follows. We defined the magnetic field measured by the m-th sensor at time t as bm(t), and a set of measured data as a column vector b(t) = [b1(t),b2(t),...,bM(t)]T where M is the total number of sensors and superscript T indicates the matrix transpose. The covariance matrix of the measurement was denoted R=btbtT where ⋅ indicates the ensemble average over trials. We assumed that the sensor data arise from elemental dipoles at each spatial location r, represented by a three-dimensional vector such that r = [rx,ry,rz]T. The orientation of each source was defined as the vector d(r) = [dx,dy,dz]T, where dx, dy, and
