Neuroimaging experiments in general, and EEG experiments in particular, must take care to avoid confounds. A recent TPAMI paper uses data that suffers from a serious previously reported confound. We demonstrate that their new model and analysis methods do not remedy this confound, and therefore that their claims of high accuracy and neuroscience relevance are invalid.

New results suggest strong limits to the feasibility of object classification from human brain activity evoked by image stimuli, as measured through EEG. Considerable prior work suffers from a confound between the stimulus class and the time since the start of the experiment. A prior attempt to avoid this confound using randomized trials was unable to achieve results above chance in a statistically significant fashion when the data sets were of the same size as the original experiments. Here, we attempt object classification from EEG using an array of methods that are representative of the state-of-the-art, with a far larger (20×) dataset of randomized EEG trials, 1,000 stimulus presentations of each of forty classes, all from a single subject. To our knowledge, this is the largest such EEG data-collection effort from a single subject and is at the bounds of feasibility. We obtain classification accuracy that is marginally above chance and above chance in a statistically significant fashion, and further assess how accuracy depends on the classifier used, the amount of training data used, and the number of classes. Reaching the limits of data collection with only marginally above-chance performance suggests that the prevailing literature substantially exaggerates the feasibility of object classification from EEG.

A recent paper [31] claims to classify brain processing evoked in subjects watching ImageNet stimuli as measured with EEG and to employ a representation derived from this processing to construct a novel object classifier. That paper, together with a series of subsequent papers [11, 18, 20, 24, 25, 30, 34], claims to achieve successful results on a wide variety of computer-vision tasks, including object classification, transfer learning, and generation of images depicting human perception and thought using brain-derived representations measured through EEG. Our novel experiments and analyses demonstrate that their results crucially depend on the block design that they employ, where all stimuli of a given class are presented together, and fail with a rapid-event design, where stimuli of different classes are randomly intermixed. The block design leads to classification of arbitrary brain states based on block-level temporal correlations that are known to exist in all EEG data, rather than stimulus-related activity. Because every trial in their test sets comes from the same block as many trials in the corresponding training sets, their block design thus leads to classifying arbitrary temporal artifacts of the data instead of stimulus-related activity. This invalidates all subsequent analyses performed on this data in multiple published papers and calls into question all of the reported results. We further show that a novel object classifier constructed with a random codebook performs as well as or better than a novel object classifier constructed with the representation extracted from EEG data, suggesting that the performance of their classifier constructed with a representation extracted from EEG data does not benefit from the brain-derived representation. Together, our results illustrate the far-reaching implications of the temporal autocorrelations that exist in all neuroimaging data for classification experiments. Further, our results calibrate the underlying difficulty of the tasks involved and caution against overly optimistic, but incorrect, claims to the contrary.

This paper presents a novel Kalman filter for the accurate determination of a vehicle's attitude (pitch and roll angles) using a low-cost MEMS inertial measurement unit (IMU) sensor, comprising a tri-axial gyroscope and a tri-axial accelerometer. Currently, vehicles deploy expensive gyroscopes for attitude determination. A low-cost MEMS gyro cannot be used because of the drift problem. Typically, an accelerometer is used to correct this drift by measuring the attitude from gravitational acceleration. This is, however, not possible in vehicular applications, because accelerometer measurements are corrupted by external accelerations produced due to vehicle movements. In this paper, we show that vehicle kinematics allow the removal of external accelerations from the lateral and vertical axis accelerometer measurements, thus giving the correct estimate of lateral and vertical axis gravitational accelerations. An estimate of the longitudinal axis gravitational acceleration can then be obtained by using the vector norm property of gravitational acceleration. A Kalman filter is designed, which implements the proposed solution and uses the accelerometer in conjunction with the gyroscope to accurately determine the attitude of a vehicle. Hence, this paper enables the use of extremely low-cost MEMS IMU for accurate attitude determination in vehicular domain for the first time. The proposed filter was tested by both simulations and experiments under various dynamic conditions and results were compared with five existing methods from the literature. The proposed filter was able to maintain sub-degree estimation accuracy even under very severe and prolonged dynamic conditions. To signify the importance of the achieved accuracy in determining accurate attitude, we investigated its use in two vehicular applications: vehicle yaw estimate and vehicle location estimate by dead reckoning and showed the performance improvements obtained by the proposed filter.

This paper presents a method for localizing a land vehicle using low cost MEMS IMU sensors and a terrain map in global positioning system (GPS) denied environment. Previous attempts to localize a vehicle based on terrain information used a tactical grade IMU which is not cost-effective on a commercial scale. On the other hand, MEMS sensors are very cheap but cannot provide accurate attitude estimation for a moving vehicle as the sensor measurements are influenced by the kinematic motion of the vehicle. Acceleration and braking corrupt the longitudinal sensor measurements and turning corrupts the lateral sensor measurements. In this paper, we formulate a Kalman filter which is able to compensate the external acceleration acting in the lateral direction using a gyroscope and odometery providing an accurate estimate of the vehicle's roll angle. A particle filter then uses this roll angle estimate along with a pre-determined terrain map of the driving region to localize the vehicle. Experimental results performed on an instrumented test vehicle show that the corrected roll measurements from MEMS IMU data using the proposed Kalman filter can localize a land vehicle very accurately.

The need of accurate and reliable positioning in various location-aware safety critical transportation applications is increasing day by day. The Global Positioning System (GPS) is not able to provide lane-level vehicle localization without the aid of differential corrections. It also suffers from signal outages in urban areas resulting in a complete loss of location information. Therefore, GPS independent localization methods are now being developed. In this domain, inertial sensors along with a terrain map have been successfully deployed to achieve sub-meter level accuracy in the longitudinal direction of the vehicle in an urban environment. However, lateral localization of the vehicle with good accuracy and computational efficiency remains a challenging topic. Existing algorithms are computationally intensive, and do not provide location information during the process of lane change by the vehicle. This information is very crucial as the risk of potential conflict with nearby vehicles is higher during lane changes. In this paper, we present a computationally efficient method for achieving lane-level localization in a multi-lane scenario by combining the particle filter with dead- reckoning. The particle filter provides the location information about a single lane while location information during the lane change maneuvers is provided by dead-reckoning. Lane- change maneuvers are detected by constantly observing the yaw rate of the vehicle. Developing a computationally efficient algorithm enables the GPS independent localization algorithm to be run on low cost micro-controllers making its deployment feasible for packaged devices. Experiments performed on an instrumented vehicle show the superiority of the proposed algorithm on the existing ones.